WO2024027566A1

WO2024027566A1 - Constraining convolution model coefficient

Info

Publication number: WO2024027566A1
Application number: PCT/CN2023/109712
Authority: WO
Inventors: Cheng-Yen Chuang; Ching-Yeh Chen
Original assignee: Mediatek Inc.
Priority date: 2022-08-02
Filing date: 2023-07-28
Publication date: 2024-02-08
Also published as: TW202412522A

Abstract

A method for performing cross component prediction by constraining the coefficients of a component prediction model is provided. A video coder receives data for a block of pixels to be encoded or decoded as a current block of a current picture of a video. The video coder derives a set of coefficients based on corresponding input and output component samples. The video coder constrains the derived set of coefficients based on a set of constraints. The video coder applies the constrained set of coefficients as a component prediction model to generate a predictor for the current block. The video coder encodes or decodes the current block by using the generated predictor.

Description

CONSTRAINING CONVOLUTION MODEL COEFFICIENT

CROSS REFERENCE TO RELATED PATENT APPLICATION (S)

The present disclosure is part of a non-provisional application that claims the priority benefit of U.S. Provisional Patent Application No. 63/370,133, filed on 2 August 2022. Contents of above-listed applications are herein incorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to video coding. In particular, the present disclosure relates to methods of coding pixel blocks by cross-component prediction.

BACKGROUND

Unless otherwise indicated herein, approaches described in this section are not prior art to the claims listed below and are not admitted as prior art by inclusion in this section.

High-Efficiency Video Coding (HEVC) is an international video coding standard developed by the Joint Collaborative Team on Video Coding (JCT-VC) . HEVC is based on the hybrid block-based motion-compensated DCT-like transform coding architecture. The basic unit for compression, termed coding unit (CU) , is a 2Nx2N square block of pixels, and each CU can be recursively split into four smaller CUs until the predefined minimum size is reached. Each CU contains one or multiple prediction units (PUs) .

Versatile video coding (VVC) is the latest international video coding standard developed by the Joint Video Expert Team (JVET) of ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11. The input video signal is predicted from the reconstructed signal, which is derived from the coded picture regions. The prediction residual signal is processed by a block transform. The transform coefficients are quantized and entropy coded together with other side information in the bitstream. The reconstructed signal is generated from the prediction signal and the reconstructed residual signal after inverse transform on the de-quantized transform coefficients. The reconstructed signal is further processed by in-loop filtering for removing coding artifacts. The decoded pictures are stored in the frame buffer for predicting the future pictures in the input video signal.

In VVC, a coded picture is partitioned into non-overlapped square block regions represented by the associated coding tree units (CTUs) . The leaf nodes of a coding tree correspond to the coding units (CUs) . A coded picture can be represented by a collection of slices, each comprising an integer number of CTUs. The individual CTUs in a slice are processed in raster-scan order. A bi-predictive (B) slice may be decoded using intra prediction or inter prediction with at most two motion vectors and reference indices to predict the sample values of each block. A predictive (P) slice is decoded using intra prediction or inter prediction with at most one motion vector and reference index to predict the sample values of each block. An intra (I) slice is decoded using intra prediction only.

A CTU can be partitioned into one or multiple non-overlapped coding units (CUs) using the quadtree (QT) with nested multi-type-tree (MTT) structure to adapt to various local motion and texture characteristics. A CU can be further split into smaller CUs using one of the five split types: quad-tree partitioning, vertical binary tree partitioning, horizontal binary tree partitioning, vertical center-side triple-tree partitioning, horizontal center-side triple-tree partitioning.

Each CU contains one or more prediction units (PUs) . The prediction unit, together with the associated CU syntax, works as a basic unit for signaling the predictor information. The specified prediction process is employed to predict the values of the associated pixel samples inside the PU. Each CU may contain one or more transform units (TUs) for representing the prediction residual blocks. A transform unit (TU) is comprised of a transform block (TB) of luma samples and two corresponding transform blocks of chroma samples and each TB correspond to one residual block of samples from one color component. An integer transform is applied to a transform block. The level values of quantized coefficients together with other side information are entropy coded in the bitstream. The terms coding tree block (CTB) , coding block (CB) , prediction block (PB) , and transform block (TB) are defined to specify the 2-D sample array of one-color component associated with CTU, CU, PU, and TU, respectively. Thus, a CTU consists of one luma CTB, two chroma CTBs, and associated syntax elements. A similar relationship is valid for CU, PU, and TU.

For each inter-predicted CU, motion parameters consisting of motion vectors, reference picture indices and reference picture list usage index, and additional information are used for inter-predicted sample generation. The motion parameter can be signalled in an explicit or implicit manner. When a CU is coded with skip mode, the CU is associated with one PU and has no significant residual coefficients, no coded motion vector delta or reference picture index. A merge mode is specified whereby the motion parameters for the current CU are obtained from neighbouring CUs, including spatial and temporal candidates, and additional schedules introduced in VVC. The merge mode can be applied to any inter-predicted CU. The alternative to merge mode is the explicit transmission of motion parameters, where motion vector, corresponding reference picture index for each reference picture list and reference picture list usage flag and other needed information are signalled explicitly per each CU.

SUMMARY

The following summary is illustrative only and is not intended to be limiting in any way. That is, the following summary is provided to introduce concepts, highlights, benefits and advantages of the novel and non-obvious techniques described herein. Select and not all implementations are further described below in the detailed description. Thus, the following summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter.

Some embodiments of the disclosure provide a method for performing cross component prediction by constraining the coefficients of a component prediction model. A video coder receives data for a block of pixels to be encoded or decoded as a current block of a current picture of a video. The video coder derives a set of coefficients based on corresponding input and output component samples. The video coder constrains the derived set of coefficients based on a set of constraints. The video coder applies the constrained set of coefficients as a component prediction model to generate a predictor for the current block. The video coder encodes or decodes the current block by using the generated predictor.

In some embodiments, the component prediction model is a cross component model that generates prediction chroma samples based on reconstructed luma samples for the current block, and the corresponding input and output component samples are corresponding luma and chroma samples of a template region neighboring the current block. The predictor may include the generated prediction chroma samples. In some embodiments, the component prediction model is a convolution model and the derived set of coefficients are derived by solving a matrix equation between the corresponding input and output component samples.

In some embodiments, the encoder constrains the derived set of coefficients by clipping a coefficient at a clipping threshold, or by clipping different coefficients at different clipping thresholds, or by confining a coefficient to a predefined range. In some embodiments, the derived set of coefficients and the constrained set of coefficients are represented in the encoder by fixed point. For fixed point representation having a fractional portion comprising N bits, the predefined range is sized based on 1 << N. In some embodiments, when a derived coefficient is out of the predefined range, the set of coefficients is set to equal to an identity filter, or a clipping operation is applied to the out-of-range coefficient, or the derived set of coefficients is not used to encode or decode the current block (CCCM mode disabled. )

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of the present disclosure. The drawings illustrate implementations of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. It is appreciable that the drawings are not necessarily in scale as some components may be shown to be out of proportion than the size in actual implementation in order to clearly illustrate the concept of the present disclosure.

FIG. 1 conceptually illustrates chroma and luma samples that are used for derivation of linear model parameters.

FIG. 2 shows an example of classifying the neighbouring samples into two groups.

FIG. 3 conceptually illustrates the spatial components of a convolutional filter.

FIG. 4 illustrates a reference area that is used to derive filter coefficients for a convolution model for a current block.

FIG. 5 conceptually illustrates a data path of a video coder that derives and constrains model coefficients.

FIG. 6 illustrates an example video encoder that may use component prediction models for encoding pixel blocks.

FIG. 7 illustrates portions of the video encoder that derives and uses a component prediction model by constraining its coefficients.

FIG. 8 conceptually illustrates a process that applies constraints to coefficients of a component prediction model.

FIG. 9 illustrates an example video decoder that may use component prediction models for encoding pixel blocks.

FIG. 10 illustrates portions of the video decoder that derives and uses a component prediction model by constraining its coefficients.

FIG. 11 conceptually illustrates a process that applies constraints to coefficients of a component prediction model.

FIG. 12 conceptually illustrates an electronic system with which some embodiments of the present disclosure are implemented.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. Any variations, derivatives and/or extensions based on teachings described herein are within the protective scope of the present disclosure. In some instances, well-known methods, procedures, components, and/or circuitry pertaining to one or more example implementations disclosed herein may be described at a relatively high level without detail, in order to avoid unnecessarily obscuring aspects of teachings of the present disclosure.

I. Cross Component Linear Model (CCLM)

Cross Component Linear Model (CCLM) or Linear Model (LM) mode is a cross component prediction mode in which chroma components of a block is predicted from the collocated reconstructed luma samples by linear models. The parameters (e.g., scale and offset) of the linear model are derived from already reconstructed luma and chroma samples that are adjacent to the block. For example, in VVC, the CCLM mode makes use of inter-channel dependencies to predict the chroma samples from reconstructed luma samples. This prediction is carried out using a linear model in the form of:
P (i, j) =α·rec′_L (i, j) +β (1)

P (i, j) in eq. (1) represents the predicted chroma samples in a CU (or the predicted chroma samples of the current CU) and rec′_L (i, j) represents the down-sampled reconstructed luma samples of the same CU (or the corresponding reconstructed luma samples of the current CU) .

The CCLM model parameters α (scaling parameter) and β (offset parameter) are derived based on at most four neighboring chroma samples and their corresponding down-sampled luma samples. In LM_Amode (also denoted as LM-T mode) , only the above or top-neighboring template is used to calculate the linear model coefficients. In LM_L mode (also denoted as LM-L mode) , only left template is used to calculate the linear model coefficients. In LM-LA mode (also denoted as LM-LT mode) , both left and above templates are used to calculate the linear model coefficients.

FIG. 1 conceptually illustrates chroma and luma samples that are used for derivation of linear model parameters. The figure illustrates a current block 100 having luma component samples and chroma component samples in 4: 2: 0 format. The luma and chroma samples neighboring the current block are reconstructed samples. These reconstructed samples are used to derive the cross-component linear model (parameters α and β) . Since the current block in in 4: 2: 0 format, the luma samples are down-sampled first before being used for linear model derivation. In the example, there are 16 pairs of reconstructed luma (down-sampled) and chroma samples neighboring the current block. These 16 pairs of luma versus chroma values are used to derive the linear model parameters.

Suppose the current chroma block dimensions are W×H, then W' and H' are set as

– W’ = W, H’ = H when LM-LT mode is applied;

– W’ = W + H when LM-T mode is applied;

– H’ = H+W when LM-L mode is applied

The above neighboring positions are denoted as S [0, -1] . . . S [W’ -1, -1] and the left neighboring positions are denoted as S [-1, 0] . . . S [-1, H’ -1] . Then the four samples are selected as

– S [W’ /4, -1] , S [3 *W’ /4, -1] , S [-1, H’ /4] , S [-1, 3 *H’ /4] when LM mode is applied (both above and left neighboring samples are available) ;

– S [W’ /8, -1] , S [3 *W’ /8, -1] , S [5 *W’ /8, -1] , S [7 *W’ /8, -1] when LM-T mode is applied (only the above neighboring samples are available) ;

– S [-1, H’ /8] , S [-1, 3 *H’ /8] , S [-1, 5 *H’ /8] , S [-1, 7 *H’ /8] when LM-L mode is applied (only the left neighboring samples are available) ;

The four neighboring luma samples at the selected positions are down-sampled and compared four times to find two larger values: x⁰ _A and x¹ _A, and two smaller values: x⁰ _B and x¹ _B. Their corresponding chroma sample values are denoted as y⁰ _A, y¹ _A, y⁰ _B and y¹ _B. Then X_A, X_B, Y_A and Y_B are derived as:
X_a = (x⁰ _A + x¹ _A +1) >>1; X_b = (x⁰ _B + x¹ _B +1) >>1; (2)
Y_a = (y⁰ _A + y¹ _A +1) >>1; Y_b = (y⁰ _B + y¹ _B +1) >>1 (3)

The linear model parameters α and β are obtained according to the following equations

β=Y_b-α·X_b (5)

The operations to calculate the α and β parameters according to eq. (4) and (5) may be implemented by a look-up table. In some embodiments, to reduce the memory required for storing the look-up table, the diff value (difference between maximum and minimum values) and the parameter α are expressed by an exponential notation. For example, diff is approximated with a 4-bit significant part and an exponent. Consequently, the table for 1/diff is reduced to 16 elements for 16 values of the significand as follows:
DivTable [ ] = {0, 7, 6, 5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 0} (6)

This reduces the complexity of the calculation as well as the memory size required for storing the needed tables.

In some embodiments, to get more samples for calculating the CCLM model parameters α and β, the above template is extended to contain (W+H) samples for LM-T mode, the left template is extended to contain (H+W) samples for LM-L mode. For LM-LT mode, both the extended left template and the extended above templates are used to calculate the linear model coefficients.

To match the chroma sample locations for 4: 2: 0 video sequences, two types of down-sampling filters are applied to luma samples to achieve 2 to 1 down-sampling ratio in both horizontal and vertical directions. The selection of down-sampling filter is specified by a sequence parameter set (SPS) level flag. The two down-sampling filters are as follows, which correspond to “type-0” and “type-2” content, respectively.
rec′_L (i, j) = [rec_L (2i-1, 2j-1) +2j-1) +2*rec_L (2i-1, 2j-1) +rec_L (2i+1, 2j-
1) +rec_L (2i-1, 2j) +rec_L (2i+1, 2j) +4] >> 3 (7)
rec′_L (i, j) = [rec_L (2i, 2j-1) +rec_L (2i-1, 2j) +4*rec_L (2i, 2j) +rec_L (2i+1, 2j) +
rec_L (2i, 2j+1) +4] >> 3 (8)

In some embodiments, the α and β parameters computation is performed as part of the decoding process, and is not just as an encoder search operation. As a result, no syntax is used to convey the α and β values to decoder.

For chroma intra mode coding, a total of 8 intra modes are allowed. Those modes include five traditional intra modes and three cross-component linear model modes (LM_LA, LM_A, and LM_L) . Chroma intra mode coding may directly depend on the intra prediction mode of the corresponding luma block. Chroma intra mode signaling and corresponding luma intra prediction modes are according to the following table:

Since separate block partitioning structure for luma and chroma components is enabled in I slices, one chroma block may correspond to multiple luma blocks. Therefore, for chroma derived mode (DM) mode, the intra prediction mode of the corresponding luma block covering the center position of the current chroma block is directly inherited.

A single unified binarization table (mapping to bin string) is used for chroma intra prediction mode according to the following table:

In the Table, the first bin indicates whether it is regular (0) or LM mode (1) . If it is LM mode, then the next bin indicates whether it is LM_CHROMA (0) or not. If it is not LM_CHROMA, next 1 bin indicates whether it is LM_L (0) or LM_A (1) . For this case, when sps_cclm_enabled_flag is 0, the first bin of the binarization table for the corresponding intra_chroma_pred_mode can be discarded prior to the entropy coding. Or, in other words, the first bin is inferred to be 0 and hence not coded. This single binarization table is used for both sps_cclm_enabled_flag equal to 0 and 1 cases. The first two bins in the table are context coded with its own context model, and the rest bins are bypass coded.

In addition, in order to reduce luma-chroma latency in dual tree, when the 64x64 luma coding tree node is not split (and ISP is not used for the 64x64 CU) or partitioned with QT, the chroma CUs in 32x32 /32x16 chroma coding tree node are allowed to use CCLM in the following way:

· If the 32x32 chroma node is not split or partitioned with QT split, all chroma CUs in the 32x32 node can use CCLM

· If the 32x32 chroma node is partitioned with Horizontal BT, and the 32x16 child node does not split or uses Vertical BT split, all chroma CUs in the 32x16 chroma node can use CCLM.

· In all the other luma and chroma coding tree split conditions, CCLM is not allowed for chroma CU.

II. Multi-Model CCLM (MMLM)

Multiple model CCLM mode (MMLM) uses two models for predicting the chroma samples from the luma samples for the whole CU. Similar to CCLM, three multiple model CCLM modes (MMLM_LA, MMLM_A, and MMLM_L) are used to indicate if both above and left neighboring samples, only above neighboring samples, or only left neighboring samples are used in model parameters derivation.

In MMLM, neighbouring luma samples and neighbouring chroma samples of the current block are classified into two groups, each group is used as a training set to derive a linear model (i.e., a particular α and β are derived for a particular group) . Furthermore, the samples of the current luma block are also classified based on the same rule for the classification of neighbouring luma samples.

FIG. 2 shows an example of classifying the neighbouring samples into two groups. Threshold is calculated as the average value of the neighbouring reconstructed luma samples. A neighbouring sample at [x, y] with Rec′_L [x, y] <= Threshold is classified into group 1; while a neighbouring sample at [x, y] with Rec′_L [x, y] > Threshold is classified into group 2. Thus, the multi-model CCLM prediction for the chroma samples is:
Pred_c [x, y] = α₁×Rec′_L [x, y] + β₁ if Rec′_L [x, y] ≤Threshold
Pred_c [x, y] = α₂×Rec′_L [x, y] + β₂ if Rec′_L [x, y] > Threshold

III. Convolutional Cross-Component Model (CCCM)

In some embodiments, a convolutional cross-component model (CCCM) is applied to improve the cross-component prediction performance. For some embodiment, the convolutional model has 7-tap filter having a 5-tap plus sign shape spatial component, a non-linear term and a bias term. The input to the spatial 5-tap component of the filter includes a center (C) luma sample (which is collocated with the chroma sample to be predicted) and the center luma sample’s above/north (N) , below/south (S) , left/west (W) and right/east (E) neighbors. FIG. 3 conceptually illustrates the spatial components of a convolutional filter. The nonlinear term (denoted as P) is represented as power of two of the center luma sample C and scaled to the sample value range of the content:
P = (C*C + midVal ) >> bitDepth (9)

Thus, for 10-bit content the non-linear term P is calculated as:
P = (C*C + 512 ) >> 10

The bias term (denoted as B) represents a scalar offset between the input and output (similarly to the offset term in CCLM) and is set to middle chroma value (512 for 10-bit content) . Output of the filter is calculated as a convolution between the filter coefficients c_i and the input values and clipped to the range of valid chroma samples:
predChromaVal = c₀C + c₁N + c₂S + c₃E + c₄W + c₅P + c₆B (10)

The filter coefficients c_i are calculated by minimizing MSE between the reconstructed (or target) chroma samples in a reference area and their corresponding predicted chroma samples. Each predicted chroma sample is generated from a collocated luma sample and its surrounding luma samples using a derived component prediction model such as Eq. (10) . Eq. (10) is a convolution model based on taps for the center sample and four surrounding samples (C, N, S, E, W) . More generally, eq. (10) can be expanded to include taps for the center sample and 8 surrounding samples (C, N, S, E, W, NE, NW, SE, SW. ) Eq. (10) and its expanded form can more generally be referred to as a component prediction model (as it can be used for cross-component prediction or intra-component prediction) .

FIG. 4 illustrates a reference area that is used to derive filter coefficients for a convolution model for a current block. The reference area includes (reference) lines of (chroma) samples above and left of the current block 400. (The current block 400 is a PU in this example) . The reference area extends one PU width to the right and one PU height below the PU boundaries. The reference area may be adjusted to include only available samples. An extension area to the reference area is used to support the “side samples” of the plus shaped spatial filter (e.g., N, E, W, S samples as described by reference to FIG. 3 above, and in addition, NW, NE, SW, SE samples) and are padded when in unavailable areas.

The MSE minimization may be performed by calculating an autocorrelation matrix for the luma input and a cross-correlation vector between the luma input and chroma output. The autocorrelation matrix is LDL decomposed and the final filter coefficients are calculated using back-substitution. The process is similar to the calculation of the ALF filter coefficients in ECM, however, in some embodiments, LDL decomposition was chosen instead of Cholesky decomposition to avoid using square root operations.

In some embodiments, a higher degree model is used to predict chroma samples, instead of the linear model. The higher degree model may include a k-tap spatial term, a non-linear term (denoted as P) , and a bias term (denoted as B) , which may be formulated as:

where rec_L' ^(i, ^j) is the down-sampled reconstructed luma sample at position (i, j) , and neiRec_L' ^(x) is one of neighboring samples surrounding rec_L' ^(i, ^j) , and a₀, a_x, b, and c are model parameters. The higher degree model can be used in deriving model parameters between color components, or between the reference samples of the current frame/picture and reference frames/pictures.

IV. Constraints on Model Coefficients

In some embodiments, after the matrix equation solving process, the final derived filter/model coefficients are used to generate a CCCM predictor. However, the derived filter coefficients may be unreasonable, such as when the model overfits on the reference samples, or when the filter coefficients are too large. Some embodiments of the disclosure provide methods to apply constraints on the derived model coefficients before generating the CCCM predictor.

FIG. 5 conceptually illustrates a data path 500 of a video coder that derives and constrains model coefficients (e.g., for CCCM) . The constrained model coefficients are used to generate a predictor for the current block. As illustrated, the data path 500 starts with a matrix preparation module 510 that generates an autocorrelation matrix and a cross correlation vector 515. The auto correlation matrix is prepared based on corresponding input component samples 505 (X samples) . The correlation vector is prepared based on corresponding input and output component samples 505 (X samples and Y samples) . Corresponding input and output component samples may be corresponding reconstructed luma and chroma samples of a reference template region neighboring the current block.

The generated auto correlation matrix and cross-correlation vector 515 are provided to a matrix equation solver module 530 to produce a set of optimized coefficients 535. A coefficient constraint module 540 applies constraints on the optimized coefficients 535 to result in a set of constrained final coefficients 545. The constrained final coefficients 545 is finally used as a component prediction model 550 (e.g., CCLM or CCCM) to generate prediction component samples 565 (e.g., chroma of the current block) based on reference component samples 560 (e.g., luma of the current block) . The generated prediction component samples 565 can be used as a CCCM predictor.

Different types of constraints can be applied on the optimized coefficients 535 to produce the constrained final coefficients 545. For example, in some embodiments, a pre-defined clipping threshold is used to clip the optimized coefficients 535 before generating the constrained final coefficients 545. In some embodiments, several clipping thresholds for the optimized coefficients 535 are pre-defined, and a syntax may be signaled for indicating the selected threshold. In some embodiments, several clipping thresholds for the optimized coefficients 535 are pre-defined, and the selected threshold may be explicitly derived from neighboring reconstructed samples or side information. In some embodiments, the clipping threshold of the different coefficients may be all different or partially different.

In some embodiments, coefficients of the component prediction model 550 are represented by fixed point, and the bit width of its integer part is constrained to a certain value. In the example, before constraint by the coefficient constraint module 540, the fixed-point format for the optimized coefficients 535 has 48 bits for the integer portion and 16 bits for the fraction portion. After constraint by the coefficient constraint module 540, the fixed-point format for the constrained coefficients 545 in the example shown in FIG. 5 has 36 bits for the integer portion and 16 bits for the fraction portion. In other example, the constrained coefficients 545 may have different bits for the integer portion. In some embodiments, the bit-width (s) of the integer and/or the fractional portion of each coefficient may be entirely different or partially different. For example, the constrained coefficients may be represented in a fixed-point format with 36 bits of integer portion and 14 bits of fraction portion.

In some embodiments, the video coder may apply a clipping operation to out-of-range coefficients. In some embodiments, if a coefficient is out-of-range, CCCM or CCLM mode may be inferred to not be enabled.

In some embodiments, the optimized coefficient 535 is clipped into one predefined range before generating the final predictor 565. For example, the range may be [-1, 1) , [-2, 2] , [-4, 4) or [-8, 8] in terms of floating-point precision. If a fixed-point precision is used to represent a model coefficient, and the number of bits in the fractional part is N bits, then the predefined range will be enlarged by (1<<N) . For example, if the supported range is [-8, 8) in floating-point precision and the number of bits in the fractional part is 5 bits, the supported range is changed from [-8, 8) in the floating-point precision to [-8*32, 8*32) in the fixed-point precision with 5-bits fractional part.

In some embodiments, the predefined range may be different among different model coefficients. For example, the predefined range depends on the spatial position of model coefficients. In some embodiments, the predefined range depends on the order of the corresponding inputs. In some embodiments, only one predefined range is used for all coefficients. In some embodiments, when one derived coefficient is out of the predefined range, the final coefficient 545 is derived by clipping the coefficient into the predefined range. In some embodiments, when one derived coefficient is out of the predefined range, the final coefficient 545 is set equal to zero. In some embodiments, when one derived coefficient is out of the predefined range, the coefficients of CCCM mode are set equal to the identity filter (i.e., no filtering applied by the CCCM mode) .

Any of the foregoing proposed methods can be implemented in encoders and/or decoders. For example, any of the proposed methods can be implemented in an inter/intra/prediction module of an encoder, and/or an inter/intra/prediction module of a decoder. Alternatively, any of the proposed methods can be implemented as a circuit coupled to the inter/intra/prediction module of the encoder and/or the inter/intra/prediction module of the decoder, so as to provide the information needed by the inter/intra/prediction module.

V. Example Video Encoder

FIG. 6 illustrates an example video encoder 600 that may use component prediction models for encoding pixel blocks. As illustrated, the video encoder 600 receives input video signal from a video source 605 and encodes the signal into bitstream 695. The video encoder 600 has several components or modules for encoding the signal from the video source 605, at least including some components selected from a transform module 610, a quantization module 611, an inverse quantization module 614, an inverse transform module 615, an intra-picture estimation module 620, an intra-prediction module 625, a motion compensation module 630, a motion estimation module 635, an in-loop filter 645, a reconstructed picture buffer 650, a MV buffer 665, and a MV prediction module 675, and an entropy encoder 690. The motion compensation module 630 and the motion estimation module 635 are part of an inter-prediction module 640.

In some embodiments, the modules 610 –690 are modules of software instructions being executed by one or more processing units (e.g., a processor) of a computing device or electronic apparatus. In some embodiments, the modules 610 –690 are modules of hardware circuits implemented by one or more integrated circuits (ICs) of an electronic apparatus. Though the modules 610 –690 are illustrated as being separate modules, some of the modules can be combined into a single module.

The video source 605 provides a raw video signal that presents pixel data of each video frame without compression. A subtractor 608 computes the difference between the raw video pixel data of the video source 605 and the predicted pixel data 613 from the motion compensation module 630 or intra-prediction module 625 as prediction residual 609. The transform module 610 converts the difference (or the residual pixel data or residual signal 608) into transform coefficients (e.g., by performing Discrete Cosine Transform, or DCT) . The quantization module 611 quantizes the transform coefficients into quantized data (or quantized coefficients) 612, which is encoded into the bitstream 695 by the entropy encoder 690.

The inverse quantization module 614 de-quantizes the quantized data (or quantized coefficients) 612 to obtain transform coefficients, and the inverse transform module 615 performs inverse transform on the transform coefficients to produce reconstructed residual 619. The reconstructed residual 619 is added with the predicted pixel data 613 to produce reconstructed pixel data 617. In some embodiments, the reconstructed pixel data 617 is temporarily stored in a line buffer (not illustrated) for intra-picture prediction and spatial MV prediction. The reconstructed pixels are filtered by the in-loop filter 645 and stored in the reconstructed picture buffer 650. In some embodiments, the reconstructed picture buffer 650 is a storage external to the video encoder 600. In some embodiments, the reconstructed picture buffer 650 is a storage internal to the video encoder 600.

The intra-picture estimation module 620 performs intra-prediction based on the reconstructed pixel data 617 to produce intra prediction data. The intra-prediction data is provided to the entropy encoder 690 to be encoded into bitstream 695. The intra-prediction data is also used by the intra-prediction module 625 to produce the predicted pixel data 613.

The motion estimation module 635 performs inter-prediction by producing MVs to reference pixel data of previously decoded frames stored in the reconstructed picture buffer 650. These MVs are provided to the motion compensation module 630 to produce predicted pixel data.

Instead of encoding the complete actual MVs in the bitstream, the video encoder 600 uses MV prediction to generate predicted MVs, and the difference between the MVs used for motion compensation and the predicted MVs is encoded as residual motion data and stored in the bitstream 695.

The MV prediction module 675 generates the predicted MVs based on reference MVs that were generated for encoding previously video frames, i.e., the motion compensation MVs that were used to perform motion compensation. The MV prediction module 675 retrieves reference MVs from previous video frames from the MV buffer 665. The video encoder 600 stores the MVs generated for the current video frame in the MV buffer 665 as reference MVs for generating predicted MVs.

The MV prediction module 675 uses the reference MVs to create the predicted MVs. The predicted MVs can be computed by spatial MV prediction or temporal MV prediction. The difference between the predicted MVs and the motion compensation MVs (MC MVs) of the current frame (residual motion data) are encoded into the bitstream 695 by the entropy encoder 690.

The entropy encoder 690 encodes various parameters and data into the bitstream 695 by using entropy-coding techniques such as context-adaptive binary arithmetic coding (CABAC) or Huffman encoding. The entropy encoder 690 encodes various header elements, flags, along with the quantized transform coefficients 612, and the residual motion data as syntax elements into the bitstream 695. The bitstream 695 is in turn stored in a storage device or transmitted to a decoder over a communications medium such as a network.

The in-loop filter 645 performs filtering or smoothing operations on the reconstructed pixel data 617 to reduce the artifacts of coding, particularly at boundaries of pixel blocks. In some embodiments, the filtering or smoothing operations performed by the in-loop filter 645 include deblock filter (DBF) , sample adaptive offset (SAO) , and/or adaptive loop filter (ALF) .

FIG. 7 illustrates portions of the video encoder 600 that derives and uses a component prediction model by constraining its coefficients. As illustrated, an initial predictor generation module 720 provides an initial predictor 715 to a component predictor model 710. The initial predictor 715 may include component samples (luma or chroma) of a reference block for predicting the component samples of the current block, or component samples of the current block for cross-component prediction. The component prediction model 710 is applied to the initial predictor 715 to generate a refined predictor 725. The samples of the refined predictor 725 may be used as the predicted pixel data 613. The initial predictor 715 may be reconstructed luma samples of the current block, while the refined predictor 725 may be prediction chroma samples of the current block.

When the current block is coded by inter-prediction, the motion estimation module 635 provides a MV that is used by the motion compensation module 630 to identify a reference block in a reference picture as the initial predictor 715. When the current block is coded by intra-prediction, the intra-prediction estimation module 620 provide an intra mode that is used by the intra-prediction module 625 to generate an intra prediction of current block as the initial predictor 715. The component samples of the initial predictor 715 may then be used as input to the component prediction model 710.

To derive the component prediction model 710, a regression data selection module 730 retrieves the required component samples from the reconstructed picture buffer 650 to serve as regression data. The regression data may be taken from regions in and/or around the current block in the current picture and in and/or around the reference block in a reference picture. The retrieved regression data (i.e., the required component samples) include corresponding input (X) and output (Y) component samples used to determine the coefficients or parameters of the component prediction model 710.

A model constructor 705 uses the regression data (X and Y) to derive the coefficients of the component prediction model 710 using techniques such as elimination method, iteration method, or decomposition method. In some embodiments, the model constructor 705 applies certain constraints to the derived coefficients before providing the constrained coefficients to be used as the component prediction model 710. The model constructor 705 may constrain the coefficients by clipping at a threshold or confine the coefficients to a predefined range. In some embodiments, the model constructor 705 may apply different clipping thresholds for different coefficients. In some embodiments, the coefficients are represented by a fixed-point representation having N-bits for its fractional portion, and the predefined range is enlarged by 1 << N. Applying constraints to the coefficients of the component prediction model is described in Section IV above.

FIG. 8 conceptually illustrates a process 800 that applies constraints to coefficients of a component prediction model. In some embodiments, one or more processing units (e.g., a processor) of a computing device implementing the encoder 600 performs the process 800 by executing instructions stored in a computer readable medium. In some embodiments, an electronic apparatus implementing the encoder 600 performs the process 800.

The encoder receives (at block 810) data to be encoded as a current block of pixels in a current picture of a video.

The encoder derives (at block 820) a set of coefficients based on corresponding input and output component samples for a component prediction model. In some embodiments, the component prediction model is a cross component model that generates prediction chroma samples based on reconstructed luma samples for the current block, and the corresponding input and output component samples are corresponding luma and chroma samples of a template region neighboring the current block. In some embodiments, the component prediction model is a convolution model and the derived set of coefficients are derived by decomposition and back substitution based on an autocorrelation matrix between the corresponding input and output component samples.

The encoder constrains (at block 830) the derived set of coefficients based on a set of constraints. In some embodiments, the encoder constrains the derived set of coefficients by clipping a coefficient at a clipping threshold, or by clipping different coefficients at different clipping thresholds, or by confining a coefficient to a predefined range. In some embodiments, the derived set of coefficients and the constrained set of coefficients are represented in the encoder by floating point. In some embodiments, the coefficients are represented by a fixed-point representation having N-bits for its fractional portion, and the predefined range is sized based on 1 << N. In some embodiments, when a derived coefficient is out of the predefined range, the set of coefficients is set to equal to an identity filter, or a clipping operation is applied to the out-of-range coefficient, or the derived set of coefficients is not used to encode or decode the current block (CCCM mode disabled. )

The encoder applies (at block 840) the constrained set of coefficients as the component prediction model to generate a predictor for the current block. The predictor may include the generated prediction chroma samples.

The encoder encodes (at block 850) the current block by using the generated predictor to produce prediction residuals.

VI. Example Video Decoder

In some embodiments, an encoder may signal (or generate) one or more syntax element in a bitstream, such that a decoder may parse said one or more syntax element from the bitstream.

FIG. 9 illustrates an example video decoder 900 that may use component prediction models for encoding pixel blocks. As illustrated, the video decoder 900 is an image-decoding or video-decoding circuit that receives a bitstream 995 and decodes the content of the bitstream into pixel data of video frames for display. The video decoder 900 has several components or modules for decoding the bitstream 995, including some components selected from an inverse quantization module 911, an inverse transform module 910, an intra-prediction module 925, a motion compensation module 930, an in-loop filter 945, a decoded picture buffer 950, a MV buffer 965, a MV prediction module 975, and a parser 990. The motion compensation module 930 is part of an inter-prediction module 940.

In some embodiments, the modules 910 –990 are modules of software instructions being executed by one or more processing units (e.g., a processor) of a computing device. In some embodiments, the modules 910 –990 are modules of hardware circuits implemented by one or more ICs of an electronic apparatus. Though the modules 910 –990 are illustrated as being separate modules, some of the modules can be combined into a single module.

The parser 990 (or entropy decoder) receives the bitstream 995 and performs initial parsing according to the syntax defined by a video-coding or image-coding standard. The parsed syntax element includes various header elements, flags, as well as quantized data (or quantized coefficients) 912. The parser 990 parses out the various syntax elements by using entropy-coding techniques such as context-adaptive binary arithmetic coding (CABAC) or Huffman encoding.

The inverse quantization module 911 de-quantizes the quantized data (or quantized coefficients) 912 to obtain transform coefficients, and the inverse transform module 910 performs inverse transform on the transform coefficients 916 to produce reconstructed residual signal 919. The reconstructed residual signal 919 is added with predicted pixel data 913 from the intra-prediction module 925 or the motion compensation module 930 to produce decoded pixel data 917. The decoded pixels data are filtered by the in-loop filter 945 and stored in the decoded picture buffer 950. In some embodiments, the decoded picture buffer 950 is a storage external to the video decoder 900. In some embodiments, the decoded picture buffer 950 is a storage internal to the video decoder 900.

The intra-prediction module 925 receives intra-prediction data from bitstream 995 and according to which, produces the predicted pixel data 913 from the decoded pixel data 917 stored in the decoded picture buffer 950. In some embodiments, the decoded pixel data 917 is also stored in a line buffer (not illustrated) for intra-picture prediction and spatial MV prediction.

In some embodiments, the content of the decoded picture buffer 950 is used for display. A display device 955 either retrieves the content of the decoded picture buffer 950 for display directly, or retrieves the content of the decoded picture buffer to a display buffer. In some embodiments, the display device receives pixel values from the decoded picture buffer 950 through a pixel transport.

The motion compensation module 930 produces predicted pixel data 913 from the decoded pixel data 917 stored in the decoded picture buffer 950 according to motion compensation MVs (MC MVs) . These motion compensation MVs are decoded by adding the residual motion data received from the bitstream 995 with predicted MVs received from the MV prediction module 975.

The MV prediction module 975 generates the predicted MVs based on reference MVs that were generated for decoding previous video frames, e.g., the motion compensation MVs that were used to perform motion compensation. The MV prediction module 975 retrieves the reference MVs of previous video frames from the MV buffer 965. The video decoder 900 stores the motion compensation MVs generated for decoding the current video frame in the MV buffer 965 as reference MVs for producing predicted MVs.

The in-loop filter 945 performs filtering or smoothing operations on the decoded pixel data 917 to reduce the artifacts of coding, particularly at boundaries of pixel blocks. In some embodiments, the filtering or smoothing operations performed by the in-loop filter 945 include deblock filter (DBF) , sample adaptive offset (SAO) , and/or adaptive loop filter (ALF) .

FIG. 10 illustrates portions of the video decoder 900 that derives and uses a component prediction model by constraining its coefficients. As illustrated, an initial predictor generation module 1020 provides an initial predictor 1015 to a component predictor model 1010. The initial predictor 1015 may include component samples (luma or chroma) of a reference block for predicting the component samples of the current block, or component samples of the current block for cross-component prediction. The component prediction model 1010 is applied to the initial predictor 1015 to generate a refined predictor 1025. The samples of the refined predictor 1025 may be used as the predicted pixel data 913. The initial predictor 1015 may be reconstructed luma samples of the current block, while the refined predictor 1025 may be prediction chroma samples of the current block.

When the current block is coded by inter-prediction, the entropy decoder 990 provides a MV that is used by the motion compensation module 930 to identify a reference block in a reference picture as the initial predictor 1015. When the current block is coded by intra-prediction, the entropy decoder 990 provide an intra mode that is used by the intra-prediction module 925 to generate an intra prediction of current block as the initial predictor 1015. The component samples of the initial predictor 1015 may then be used as input to the component prediction model 1010.

To derive the component prediction model 1010, a regression data selection module 1030 retrieves the required component samples from the decoded picture buffer 950 to serve as regression data. The regression data may be taken from regions in and/or around the current block in the current picture and in and/or around the reference block in a reference picture. The retrieved regression data (i.e., the required component samples) include corresponding input (X) and output (Y) component samples used to determine the coefficients or parameters of the component prediction model 1010.

A model constructor 1005 uses the regression data (X and Y) to derive the coefficients of the component prediction model 1010 using techniques such as elimination method, iteration method, or decomposition method. In some embodiments, the model constructor 1005 applies certain constraints to the derived coefficients before providing the constrained coefficients to be used as the component prediction model 1010. The model constructor 1005 may constrain the coefficients by clipping at a threshold or confine the coefficients to a predefined range. In some embodiments, the model constructor 1005 may apply different clipping thresholds for different coefficients. In some embodiments, the coefficients are represented by a fixed-point representation having N-bits for its fractional portion, and the predefined range is enlarged by 1 << N. Applying constraints to the coefficients of the component prediction model is described in Section IV above.

FIG. 11 conceptually illustrates a process 1100 that applies constraints to coefficients of a component prediction model. In some embodiments, one or more processing units (e.g., a processor) of a computing device implementing the decoder 900 performs the process 1100 by executing instructions stored in a computer readable medium. In some embodiments, an electronic apparatus implementing the decoder 900 performs the process 1100.

The decoder receives (at block 1110) data to be decoded as a current block of pixels in a current picture of a video.

The decoder derives (at block 1120) a set of coefficients based on corresponding input and output component samples for a component prediction model. In some embodiments, the component prediction model is a cross component model that generates prediction chroma samples based on reconstructed luma samples for the current block, and the corresponding input and output component samples are corresponding luma and chroma samples of a template region neighboring the current block. In some embodiments, the component prediction model is a convolution model and the derived set of coefficients are derived by decomposition and back substitution based on an autocorrelation matrix between the corresponding input and output component samples.

The decoder constrains (at block 1130) the derived set of coefficients based on a set of constraints. In some embodiments, the decoder constrains the derived set of coefficients by clipping a coefficient at a clipping threshold, or by clipping different coefficients at different clipping thresholds, or by confining a coefficient to a predefined range. In some embodiments, the derived set of coefficients and the constrained set of coefficients are represented in the decoder by floating point. In some embodiments, the coefficients are represented by a fixed-point representation having N-bits for its fractional portion, and the predefined range is sized based on 1 << N. In some embodiments, when a derived coefficient is out of the predefined range, the set of coefficients is set to equal to an identity filter, or a clipping operation is applied to the out-of-range coefficient, or the derived set of coefficients is not used to encode or decode the current block (CCCM mode disabled. )

The decoder applies (at block 1140) the constrained set of coefficients as the component prediction model to generate a predictor for the current block. The predictor may include the generated prediction chroma samples.

The decoder reconstructs (at block 1150) the current block by using the generated predictor. The decoder may then provide the reconstructed current block for display as part of the reconstructed current picture.

VII. Example Electronic System

Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a computer readable storage medium (also referred to as computer readable medium) . When these instructions are executed by one or more computational or processing unit (s) (e.g., one or more processors, cores of processors, or other processing units) , they cause the processing unit (s) to perform the actions indicated in the instructions. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, random-access memory (RAM) chips, hard drives, erasable programmable read only memories (EPROMs) , electrically erasable programmable read-only memories (EEPROMs) , etc. The computer readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections.

In this specification, the term “software” is meant to include firmware residing in read-only memory or applications stored in magnetic storage which can be read into memory for processing by a processor. Also, in some embodiments, multiple software inventions can be implemented as sub-parts of a larger program while remaining distinct software inventions. In some embodiments, multiple software inventions can also be implemented as separate programs. Finally, any combination of separate programs that together implement a software invention described here is within the scope of the present disclosure. In some embodiments, the software programs, when installed to operate on one or more electronic systems, define one or more specific machine implementations that execute and perform the operations of the software programs.

FIG. 12 conceptually illustrates an electronic system 1200 with which some embodiments of the present disclosure are implemented. The electronic system 1200 may be a computer (e.g., a desktop computer, personal computer, tablet computer, etc. ) , phone, PDA, or any other sort of electronic device. Such an electronic system includes various types of computer readable media and interfaces for various other types of computer readable media. Electronic system 1200 includes a bus 1205, processing unit (s) 1210, a graphics-processing unit (GPU) 1215, a system memory 1220, a network 1225, a read-only memory 1230, a permanent storage device 1235, input devices 1240, and output devices 1245.

The bus 1205 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system 1200. For instance, the bus 1205 communicatively connects the processing unit (s) 1210 with the GPU 1215, the read-only memory 1230, the system memory 1220, and the permanent storage device 1235.

From these various memory units, the processing unit (s) 1210 retrieves instructions to execute and data to process in order to execute the processes of the present disclosure. The processing unit (s) may be a single processor or a multi-core processor in different embodiments. Some instructions are passed to and executed by the GPU 1215. The GPU 1215 can offload various computations or complement the image processing provided by the processing unit (s) 1210.

The read-only-memory (ROM) 1230 stores static data and instructions that are used by the processing unit (s) 1210 and other modules of the electronic system. The permanent storage device 1235, on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the electronic system 1200 is off. Some embodiments of the present disclosure use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the permanent storage device 1235.

Other embodiments use a removable storage device (such as a floppy disk, flash memory device, etc., and its corresponding disk drive) as the permanent storage device. Like the permanent storage device 1235, the system memory 1220 is a read-and-write memory device. However, unlike storage device 1235, the system memory 1220 is a volatile read-and-write memory, such a random access memory. The system memory 1220 stores some of the instructions and data that the processor uses at runtime. In some embodiments, processes in accordance with the present disclosure are stored in the system memory 1220, the permanent storage device 1235, and/or the read-only memory 1230. For example, the various memory units include instructions for processing multimedia clips in accordance with some embodiments. From these various memory units, the processing unit (s) 1210 retrieves instructions to execute and data to process in order to execute the processes of some embodiments.

The bus 1205 also connects to the input and output devices 1240 and 1245. The input devices 1240 enable the user to communicate information and select commands to the electronic system. The input devices 1240 include alphanumeric keyboards and pointing devices (also called “cursor control devices” ) , cameras (e.g., webcams) , microphones or similar devices for receiving voice commands, etc. The output devices 1245 display images generated by the electronic system or otherwise output data. The output devices 1245 include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD) , as well as speakers or similar audio output devices. Some embodiments include devices such as a touchscreen that function as both input and output devices.

Finally, as shown in FIG. 12, bus 1205 also couples electronic system 1200 to a network 1225 through a network adapter (not shown) . In this manner, the computer can be a part of a network of computers (such as a local area network ( “LAN” ) , a wide area network ( “WAN” ) , or an Intranet, or a network of networks, such as the Internet. Any or all components of electronic system 1200 may be used in conjunction with the present disclosure.

Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media) . Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM) , recordable compact discs (CD-R) , rewritable compact discs (CD-RW) , read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM) , a variety of recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc. ) , flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc. ) , magnetic and/or solid state hard drives, read-only and recordablediscs, ultra-density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media may store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor or multi-core processors that execute software, many of the above-described features and applications are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) . In some embodiments, such integrated circuits execute instructions that are stored on the circuit itself. In addition, some embodiments execute software stored in programmable logic devices (PLDs) , ROM, or RAM devices.

As used in this specification and any claims of this application, the terms “computer” , “server” , “processor” , and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms display or displaying means displaying on an electronic device. As used in this specification and any claims of this application, the terms “computer readable medium, ” “computer readable media, ” and “machine readable medium” are entirely restricted to tangible, physical objects that store information in a form that is readable by a computer. These terms exclude any wireless signals, wired download signals, and any other ephemeral signals.

While the present disclosure has been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the present disclosure can be embodied in other specific forms without departing from the spirit of the present disclosure. In addition, a number of the figures (including FIG. 8 and FIG. 11) conceptually illustrate processes. The specific operations of these processes may not be performed in the exact order shown and described. The specific operations may not be performed in one continuous series of operations, and different specific operations may be performed in different embodiments. Furthermore, the process could be implemented using several sub-processes, or as part of a larger macro process. Thus, one of ordinary skill in the art would understand that the present disclosure is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims.

Additional Notes

The herein-described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely examples, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermediate components. Likewise, any two components so associated can also be viewed as being "operably connected" , or "operably coupled" , to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably couplable" , to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

Further, with respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

Moreover, it will be understood by those skilled in the art that, in general, terms used herein, and especially in the appended claims, e.g., bodies of the appended claims, are generally intended as “open” terms, e.g., the term “including” should be interpreted as “including but not limited to, ” the term “having” should be interpreted as “having at least, ” the term “includes” should be interpreted as “includes but is not limited to, ” etc. It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to implementations containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an, " e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more; ” the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number, e.g., the bare recitation of "two recitations, " without other modifiers, means at least two recitations, or two or more recitations. Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc. ” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention, e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc. In those instances where a convention analogous to “at least one of A, B, or C, etc. ” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention, e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc. It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B. ”

From the foregoing, it will be appreciated that various implementations of the present disclosure have been described herein for purposes of illustration, and that various modifications may be made without departing from the scope and spirit of the present disclosure. Accordingly, the various implementations disclosed herein are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

A video coding method comprising:

receiving data for a block of pixels to be encoded or decoded as a current block of a current picture of a video;

deriving a set of coefficients based on corresponding input and output component samples;

constraining the derived set of coefficients based on a set of constraints;

applying the constrained set of coefficients as a component prediction model to generate a predictor for the current block; and

encoding or decoding the current block by using the generated predictor.
The video coding method of claim 1, wherein the component prediction model is a cross component model that generates prediction chroma samples for the predictor based on reconstructed luma samples of the current block.
The video coding method of claim 1, wherein the corresponding input and output component samples are corresponding luma and chroma samples of a template region neighboring the current block.
The video coding method of claim 1, wherein the component prediction model is a convolution model and the derived set of coefficients are derived by solving a matrix equation between the corresponding input and output component samples.
The video coding method of claim 1, wherein constraining the derived set of coefficients comprises clipping a coefficient at a clipping threshold.
The video coding method of claim 1, wherein constraining the derived set of coefficients comprises clipping different coefficients at different clipping thresholds.
The video coding method of claim 1, wherein the derived set of coefficients and the constrained set of coefficients are represented in a video coder by floating point.
The video coding method of claim 1, wherein constraining the derived set of coefficients comprises confining a coefficient to a predefined range.
The video coding method of claim 8, wherein the derived set of coefficients and the constrained set of coefficients are represented in a video coder by a fixed point representation having a fractional portion comprising N bits, wherein the predefined range is sized based on 1 << N.
The video coding method of claim 8, wherein when a derived coefficient is out of the predefined range, the set of coefficients is set to equal to an identity filter.
The video coding method of claim 8, wherein when a derived coefficient is out of the predefined range, a clipping operation is applied to the out-of-range coefficient.
The video coding method of claim 8, wherein when a derived coefficient is out of the predefined range, the derived set of coefficients is not used to encode or decode the current block.
An electronic apparatus comprising:

a video coder circuit configured to perform operations comprising:

receiving data for a block of pixels to be encoded or decoded as a current block of a current picture of a video;

deriving a set of coefficients based on corresponding input and output component samples;

constraining the derived set of coefficients based on a set of constraints;

applying the constrained set of coefficients as a component prediction model to generate a predictor for the current block; and

encoding or decoding the current block by using the generated predictor.
A video decoding method comprising:

receiving data for a block of pixels to be decoded as a current block of a current picture of a video;

deriving a set of coefficients based on corresponding input and output component samples;

constraining the derived set of coefficients based on a set of constraints;

applying the constrained set of coefficients as a component prediction model to generate a predictor for the current block; and

reconstructing the current block by using the generated predictor.
A video encoding method comprising:

receiving data for a block of pixels to be encoded as a current block of a current picture of a video;

deriving a set of coefficients based on corresponding input and output component samples;

constraining the derived set of coefficients based on a set of constraints;

applying the constrained set of coefficients as a component prediction model to generate a predictor for the current block; and

encoding the current block by using the generated predictor.