Image and Video Compression

In this paper, we derive a principal component regression (PCR) method for estimating the optical flow between frames of video sequences according to a pel-recursive manner. This is an easy alternative to dealing with mixtures of motion vectors due to the lack of too much prior information on their statistics (although they are supposed to be normal). The 2D motion vector estimation takes into consideration local image properties. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Preliminary experiments indicate that this approach provides robust estimates of the optical flow. KEY WORDS Motion estimation, principal component regression, and surveillance. 1.

In this paper image compression is performed based on vector quantization(VQ) technique by using Linde- Buzo-Gray algorithm. A vector quantizer based on self organizing map(SOM) is implemented to obtain a optimized values of area, power and delay. The blocks designed using the verilog program involves squared difference unit block, arithmetic block, minimum distance search circuit. The ASIC parameters such as power, area and delay of each block are calculated using Cadence-EDA tool.

In recent trends, digital information to the industrial integration for the intelligent transportation system (ITS) field is gaining importance for the researcher, academia, and industrial persons. Visual information helps to manage traffic systems in the industrial forum to build smart cities in developed countries. This paper presents vision-based real-time vehicle detection and Vehicle Speed Measurement (VSM) using morphology operation and binary logical process for an unplanned traffic scenario using image processing techniques. Vehicle detection and VSM help to reduce the number of accidents and improve road network efficiency. The bounding box size for vehicle detection is flexible according to vehicles' sizes on the road. We test this system with different colors and dimensions for a selected Region of Interest (ROI). The ROI sets using the two-line approach. Here, we compare the proposed system with the inter-frame difference method and the blob analysis method with recall, precision, and F1 performance parameters.

It's a fact that nonlinear color models like Hue-Saturation -Value/ Brightness/ Luminance/ Intensity (HSV/ HSB/ HSL/ HSI) have special feature for each
channel. So in this paper we propose a new hybrid compression system that deals with each channel with a suitable compression technique to obtain encoded images with less size and high decoding quality than the traditional encoding methods.

—The most significant parameters of image processing are image resolution and speed of processing. Compressing the multimedia datasets, which are rich in quality and volume is challenging. Wavelet based image compression techniques are the best tools for lossless image compression, however, they suffer by low compression ratio. Conversely fractional cosine transform based compression is a lossy compression technique with less image quality. In this paper, an improved compression technique is proposed by using wavelet transform and discrete fractional cosine transform to achieve high quality of reconstruction of an image at high compression rate. The algorithm uses wavelet transform to decompose image into frequency spectrum with low and high frequency sub bands. Application of quantization process for both sub bands at two levels increases the number of zeroes, however rich zeroes from high frequency sub bands are eliminated by creating the blocks and then storing only non-zero values and kill all blocks with zero values to form reduced array. The arithmetic coding method is used to encode the sub bands. The Experimental results of proposed method are compared with its primitive two dimensional fractional cosine and fractional Fourier compression algorithms and some significant improvements can be observed in peak signal to noise ratio and self-similarity index mode at high compression ratio.

Encoding images containing mixed content in web compatible formats like PNG, GIF and JPEG poses challenges in maintaining the quality along with good compression. In this paper, we describe a method for improving the compression of web compatible compound images while retaining high quality. The proposed method is based on the principle that the compression ratio of a given image depends upon both the image content and the compression algorithm used. Therefore, encoding various tiled regions in a given image with the algorithm appropriate to the content in the region improves the overall compression of the image. Further optimizations like sharing of indexed colors between the various tiles in the same image and removal of duplicate tiles improve the compression. This method would support both lossy and loss less compression. The resulting encoded data can be rendered by a web browser supporting CSS background-cropping or HTML5 canvas APIs.

Available in Google Play store: https://play.google.com/store/books/details/BANERJI_MULTIMEDIA_TECHNOLOGIES?id=3PIHejE3tysC&hl=en_US

This book is designed to provide comprehensive of Multimedia Technologies with a strong focus on field practice and active experimentation. Amply supplemented with rich pedagogy, it offers an unparalleled learning experience to both students and practitioners.
Available both in eBook and Print format. Concise coverage and provides practical tips for laboratory assignments using MATLAB, Adobe Flash, HTML, and other popular tools. Foreword by Prof. Philip Barker, School of Computing, University of Teesside, Middlesbrough, UK.
ISBN: 9781259083310

Reviews:
Indian Journal of Open Learning, (19(1), 2010: http://journal.ignouonline.ac.in/iojp/index.php/IJOL/article/view/113-
Journal of Educational Technology & Society, 13(2), 2010: http://www.ifets.info/journals/13_2/20.pdf-
Featured book by MathWorks, MATLAB & Simulink Based Books: http://www.mathworks.com/support/books/book49126.html

Since the  first commercially viable video codec formats appeared in the early 1990s, we have seen the emergence of a plethora of compressed digital video formats, from MPEG-1 and MPEG-2 to recent codecs such as HEVC and VP9. Each new format offers certain advantages over its predecessors. However, the increasing variety of codec formats poses many questions for anyone involved in collecting, archiving and delivering digital video content, such as:
■  Which codec format (if any) is best?
■  What is a suitable acquisition protocol for digital video?
■  Is it possible to ensure that early ‘born digital’ material will still be playable in future
decades?
■  What are the advantages and disadvantages of converting (transcoding) older formats
into newer standards?
■  What is the best way to deliver video content to end-users?
In this article I explain how a video compression codec works and consider some of the practical concerns relating to choosing and controlling a codec. I discuss the motivations behind the continued development of new codec standards and suggest practical measures to help deal with the questions listed above.

The Huffman encoding algorithm starts by constructing a list of all the alphabet symbols in descending order of their probabilities. It is a compression scheme which is used to compress the repeated data on the basis of probability. In this paper the matlab code for Huffman coding is presented.

Video compression plays a vital part in many digital video processing for applications such as digital video transmission, also thousands of websites like YouTube, Netflix etc. that requires large storage space. Video compression technologies are about reducing and removing redundant video data so that a digital video file can be effectively sent over a network or can be put in storage on computer disks with the reduction of data size. In this research paper video compression using motion compensation technique that reduces video data based on motion estimation from one frame to another, is proposed. Diamond Search (DS) motion compensation is an algorithmic technique employed for the encoding of video data for video compression. Motion compensation vectors describe a frame in terms of the transformation of a reference frame with respect to the current frame. The reference frame may be previous in time or even from the future. The proposed method reduces the searching of compression portion based on DS Algorithm in temporal redundancy video sequences. Motion blocks are further compressed by using the Scalable Video Compression methods, Adaptive Dual Tree Complex Wavelet Transform (ADT-CWT) and SPIHT. The performance of the proposed methodology is evaluated in terms of the peak signal-to-noise ratio (PSNR) and the compression ratio (CR).

Real-time vision is central to many embedded applications (e.g. vehicle guidance). It is a computationally intensive task well beyond current general purpose computing platforms such as PCs and workstations. Thus, most real time vision systems need special high performance computing platforms, commonly provided in the form of parallel processing engines or dedicated hardware. The proposed architecture uses new generations of re-programmable logic devices and modularised hardware, thereby gaining the performance advantage of hard-wired logic with the flexibility and associated economies of programmable systems. The architecture takes the form of an extensive processing hierarchy consisting of a set of tightly coupled parallel processors, each processing a portion of the image using a classic pipeline arrangement. A programmable image splitting (and reconstruction) engine feeds this array and offers the potential of further enhancing the performance of the engines by restructuring the...

Without comprising the quality of reconstructed image, image compression methods reduce the size of image.Lossy and Lossless image compression methods are available. The redundant and irrelevant information is discarded during compression. Since multimedia data is increased day by day. Therefore it is needful to compress data so that it take less memory space in computer as well as transfer of image is easy from one node to another over Internet. Many image compression techniques are available to compress image. We have proposed a new technique in this paper which gives high PSNR value which is better than previous existing methods.

Video has become an increasingly important medium for the gathering and sharing of information. The presence of videos in everything from the casual, like Youtube, to the professional, like NASA’s Mars rovers, gives any advancement in the field greater potential. However, the versatility of video is severely restricted by many factors, the most significant of which are bandwidth and digital storage. Although advances are constantly being made in these areas, the extremely high bit-rates and large storage capacities required to continuously record video are financially prohibitive. In order to address this limitation, we create a methodology in which we reduce the color palette of a video through a process called color quantization and later restore lost detail through the technique dithering in which different shades of colors can be represented through a diffusion of colors from the quantized color palette. This process has far reaching applications, but is specifically targeted at security cameras where color quantized footage is stored on the video server and when the video is played back, dithering restores much of the original quality. Consequently, file size is conserved on the server and the video is still detailed.

A compressão de dados é um dos factores que mais contribuiu para o grande crescimento das tecnologias da informação e da comunicação. Sem compressão, a maioria dos produtos tecnológicos de consumo e entretenimento, que são hoje banais, nunca teria chegado a existir, destacando-se, entre muitos outros, o DVD, as câmaras fotográficas digitais, os leitores de MP3, o YouTube e o streaming de vídeo, as redes sem fios e a televisão digital. De facto, as tecnologias de compressão multimédia permitem representar a informação de uma forma mais eficiente, reduzindo os grandes volumes de espaço de armazenamento que ocupa e, portanto, a largura de banda que consome para se transmitir nas redes e na Internet. Este livro tem como objectivo principal apresentar uma introdução fundamentada, clara, acessível e abrangente aos conceitos e tecnologias relacionadas com métodos, algoritmos e normas internacionais de compressão de informação multimédia. Ao longo do livro são abordados, entre outros, os seguintes temas: Conceitos e normas sobre compressão de informação multimédia, incluindo texto, imagens, áudio e vídeo; Entropia e algoritmos de compressão sem perdas: Shannon-Fano, Huffman, Aritmética e LZW; Psicoacústica, métodos e normas de compressão de áudio, incluindo voz (ADPCM, vocoders) e áudio de alta-fidelidade (transformadas, MP3, AAC, MP4 e Dolby AC-3); Teoria rate-distortion, métodos de compressão de imagem (DPCM, transformadas DCT, waveletet Haar, quantificadores lineares e não-lineares); Normas internacionais JPEG e JPEG2000; Compressão de vídeo, redundância espacial e temporal, quantificação de vídeo para aplicações multimédia e de videoconferência; Normas MPEG.

Compression is a technology for reducing the quantity of data used to represent any content without excessively reducing the quality of the picture. The need for an efficient technique for compression of images ever increasing because the raw images need large amounts of disk space seems to be a big disadvantage during transmission & storage. Compression is a
technique that makes storing easier for large amount of data. It
also reduces the number of bits required to store and transmit
digital media. In this paper, a fast lossless compression scheme is presented and named as HL which consists of two stages. In the first stage, a Huffman coding is used to compress the image. In the second stage all Huffman code words are concatenated together and then compressed with Lempel Ziv coding. This technique is simple in implementation and utilizes less memory. A software algorithm has been developed and implemented to compress and decompress the given image using Huffman coding techniques in MATLAB software.

This paper gives the idea of recent developments in the field of image security and improvements in image security. Images are used in many applications and to provide image security using image encryption and authentication. Image encryption techniques scramble the pixels of the image and decrease the correlation among the pixels, such that the encrypted image cannot be accessed by unauthorized user. This study proposes a method for encrypting the sender’s messages using new algorithm called chaotic encryption method.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

In today’s world of electronic revolution, digital images play an important role in consumer electronics. The need for storing
images grows day to day. Compression of digital images plays an important role in storing the images. Due to compression the
memory occupied by the images gets reduced and so that we can store more images in the available memory space. Also
compression results in encoding of images which itself offer a security on transaction. This paper deals with a clear note on
various aspects regarding compression and various compression techniques used and their implementation.

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.

Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.

This paper addresses about various image compression techniques. On the basis of analyzing the various image compression techniques this paper presents a survey of existing research papers. In this paper we analyze different types of existing method of image compression. Compression of an image is significantly different then compression of binary raw data. To solve these use different types of techniques for image compression. Now there is question may be arise that how to image compress and which types of technique is used. For this purpose there are basically two types are method are introduced namely lossless and lossy image compression techniques. In present time some other techniques are added with basic method. In some area neural network genetic algorithms are used for image compression.

https://sites.google.com/site/ijcsis/

In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM have been compared. This comparison was conducted to seek a robust method that would produce good classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG) signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder with SVM has been proposed. The EEG dataset used in this research was created by the University of Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature engineering. However, our prosed method of autoencoder in combination with SVM produced a similar accuracy of 65% without using any feature engineering technique. This research shows that this system of classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

Abstract— Raw images that are captured by cameras or created digitally have lot of excess and redundant information that are not processed by the human eye and that does not hamper visual perception of the image upon their elimination. Image compression techniques removes these excess information to make the image less in size and easily transferable. Motivation behind this work is to develop a more efficient and simple compression method combining two standard techniques viz. Run-length wavelet filters and DWT on the image and after quantizing, we applied our RH Code to encode the image. It is observed from the results that the proposed approach outperforms state-of –art and Huffman Coding after analyzing it for grayscale images on various parameters. Here we have used different methods.
Keywords- Runlength coding, Huffman coding, image compression, lossy compression, lossless compression.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

‘Born digital’ often means ‘born compressed’ and it is increasingly likely that newly-created digital video material will have gone through at least some level of lossy compression. For this reason, it is important to understand the effect of video compression on visual quality. In this paper, I will introduce the concept of video compression and its relationship to video image quality. I will consider the factors that influence visual quality, including technical factors such as codecs and coding parameters, as well as the complex and only partly-understood factors that govern our perception of moving images. I will introduce methods of measuring and quantifying video quality and show how it is possible to compare the quality and performance of video processing systems, despite the limitations of quality measurement.

This paper describes a technique to compress images based on vector quantization. The vector quantizer is designed to reduce both perceptual irrelevancy and mathematical redundancy. This is done without using transforms and entropic coding, which are normally used respectively prior and after quantization. Because of its structure, the vector quantizer can be implemented efficiently as a uniform product vector quantizer

This paper deals with the experimental study of concrete by partial replacement of fine aggregate with copper slag and glass powder to increase the strength parameters such as compressive strength, split tensile strength, and flexural strengths of the M30 concrete. Here the fine aggregate partially replaced by copper slag (CS) and glass powder (GP) in CC,10%,20%,30%, & 40%. by weight of fine aggregate. Mix design was prepared for M30 grade of concrete. By replacement of copper slag and glass powder as fine aggregate we can reduce the digging of river sand or natural sand which effects the hydraulic structure stability and as well as we can reduce the open land fill and environmental pollution with copper slag usage it in concrete. Graphs are drawn strength vs. replacement with M Sand results are compared with normal concrete. Copper slag and glass powder GP are excellent by-product or waste material which retains its original properties. Due to its chemical composition which includes high iron, silica and aluminum oxide content, it can be used as a partial replacement for fine aggregate in concrete mixes. The cube, beam and cylindrical specimens were then prepared, demolded after 24 hours and properly cured. The specimens were subjected to compression testing, split tensile strength testing and flexural testing at 7 and 28 days. The addition of addition of copper slag increased the concrete strength gradually and the maximum strength was achieved at 40% replacement by Copper Slag with fine aggregate. The addition of glass powder increased the strength gradually till 30% replacement with fine aggregate and the strength decreased at 40%. The Copper slag seems to show better strength at 40% than the glass powder at 30%.

Citra atau gambar adalah alat yang manusia pakai untuk menyampaikan pesan kepada manusia lainnya. Gambar atau citra telah berkembang seiring dengan perkembangan peradaban manusia. Bentuknya tidak lagi hanya lukisan seperti zaman prasejarah. Kini ada foto, gambar yang dihasilkan dengan menangkap cahaya pada medium yang telah dilapisi bahan kimia peka cahaya atau sensor digital, lalu ada film, gambar yang bergerak. Besarnya ukuran data yang harus dikirim melampaui kecepatan transmisi yang dimiliki oleh perangkat keras yang ada, sehingga masih terdapat delay time yang relatif besar. Selain itu media penyimpanan seperti floppy disk, hard disk, dan compact disk mempunyai kapasitas yang terbatas. Oleh karena itu, untuk mengatasi
masalah ini digunakanlah kompresi data. Ada banyak sekali teori dan metode untuk kompresi data, di antaranya metode Huffman, Run-Length
Encoding (RLE), Lempel-Zip-Welch (LZW), Shanon-Fano, dan beberapa metode lainnya. Umtuk membandingkan hasil dari kompresi citra algoritma Huffman dan algoritma LZW maka digunakan metode
eksponensial.
Kata Kunci : Citra, kompresi, algoritma Huffman, Algoritma LZW, Metode Eksponensial

In this paper, a new fractal image compression algorithm is proposed, in which the
time of the encoding process is considerably reduced. The algorithm exploits a domain pool
reduction approach, along with the use of innovative predefined values for contrast scaling
factor, S, instead of searching it across. Only the domain blocks with entropy greater than a
threshold are considered to belong to the domain pool. The algorithm has been tested for some
well-known images and the results have been compared with the state-of-the-art algorithms. The
experiments show that our proposed algorithm has considerably lower encoding time than the
other algorithms giving approximately the same quality for the encoded images.

In this thesis, we address two problems in medical image processing: (1) Lossless compression of 3D MR images (2) Modeling of fMRI time series for the estimation of haemodynamic response function (HRF).

For the lossless compression of 3D MR images, we propose two schemes. In the first scheme, a nonuniformly sized mesh is superimposed on the image, designed based on the content of the image. Affine transformations are used to estimate the mesh elements of a MR slice using the previous slice. The residue between the actual slice and the estimated slice is entropy coded. Context based entropy coding is used to enhance the performance of the entropy coding. The second is a wavelet based scheme wherein important functionalities like region of interest coding, progressive transmission and lossless compression capability are provided. 2D and 3D separable integer wavelet transforms are used for lossless compression. A context based entropy coding scheme is also used here to code the resulting bit stream. These two schemes are tested on two datasets and compared with other state-of-the-art schemes available in the literature. The bit rate obtained with our schemes of about 2 bits per voxel is comparable to the other schemes.

For estimating the HRF from fMRI data, we proposed two probability models. In the first model, the fMRI time series is assumed to be a stationary model. Probabilistic principal component analysis is used to model the time series. Using this model, one can represent trends and other confounding signals in the fMRI time series in terms of its dominant eigen vectors. The HRF function is modeled using both parametric and nonparametric models. In the second model, the time series is assumed to be non-stationary and is modeled as a fractional Brownian motion. We use the property that wavelet transform with sufficient number of vanishing moments decorrelates fBm processes. The methods are tested both on synthetic data at various SNRs and on a real null fMRI data. We compare these methods with an existing method, where the trends in the fMRI time series are modeled by polynomial bases. The proposed methods performed better with respect to the accuracy of HRF estimation. We also developed an algorithm which classifies fMRI voxels according to their HRF characteristics. This algorithm will be useful in the event related design paradigms. In this scheme, we model the class labels as a Markov random field and the class labels and HRFs are estimated iteratively using an expectation maximization algorithm. This algorithm is tested on a real null fMRI data.

With the onslaught of multimedia in the near past, there has been a tremendous increase in the uses of images. A very good example of which is the web on which most of the documents contain images. Other than this the images are being used in other applications like weather forecasting, medical diagnosis, police department. In R-Tree implementation of image database, images are made available to the program which are then stores in the database. The image database is presented using R-tree and the database is stored in separate file .This R-tree implementation results in both update as well as efficient retrieval of images from hard disk [1][2][4]. We use the similarity based retrieval feature to retrieve the required number of similar images being inquired by the user [3][5][6]. Distance matrix approach is used to find similarity of images [7]. Sobel edge detection algorithm is used to form sketches. If sketch of image is entered for similarity based retrieval, then sketches of stored images are formed and these sketches are compared with input image (sketch) using distance matrix approach[8][9].

It is an experimental design problem in which there are two Poisson sources with two possible and known rates, and one counter. Through a switch, the counter can observe the sources individually or the counts can be combined so that the counter observes the sum of the two. The sensor scheduling problem is to determine an optimal proportion of the available time to be allocated toward individual and joint sensing, under a total time constraint. Two different metrics are used for optimization: mutual information between the sources and the observed counts, and probability of detection for the associated source detection problem. Our results, which are primarily computational, indicate similar but not identical results under the two cost functions.

A number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.