research-article

Deep-Learning-Based Lossless Image Coding

Authors:

Adrian MunteanuAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 30, Issue 7

Pages 1829 - 1842

https://doi.org/10.1109/TCSVT.2019.2909821

Published: 01 July 2020 Publication History

Abstract

This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec to encode the prediction errors. First, a novel deep learning-based predictor is proposed to estimate the residuals produced by traditional prediction methods. It is shown that the use of a deep-learning paradigm substantially boosts the prediction accuracy compared with the traditional prediction methods. Second, the prediction error is modeled by a context modeling method and encoded using a novel context-tree-based bit-plane codec. Codec profiles performing either one or two coding passes are proposed, trading off complexity for compression performance. The experimental evaluation is carried out on three different types of data: photographic images, lenslet images, and video sequences. The experimental results show that the proposed lossless coding approach systematically and substantially outperforms the state-of-the-art methods for each type of data.

References

[1]

S. Chandra and W. W. Hsu, “Lossless medical image compression in a block-based storage system,” in Proc. Data Compress. Conf., Snowbird, UT, USA, Mar. 2014, p. 400.

[2]

L. F. R. Lucas, N. M. M. Rodrigues, L. A. da Silva Cruz, and S. M. M. de Faria, “Lossless compression of medical images using 3-D predictors,” IEEE Trans. Med. Imag., vol. 36, no. 11, pp. 2250–2260, Nov. 2017.

[3]

H. Wu, X. Sun, J. Yang, W. Zeng, and F. Wu, “Lossless compression of JPEG coded photo collections,” IEEE Trans. Image Process., vol. 25, no. 6, pp. 2684–2696, Jun. 2016.

Digital Library

[4]

V. Trivedi and H. Cheng, “Lossless compression of satellite image sets using spatial area overlap compensation,” in Image Analysis and Recognition. M. Kamel and A. Campilho, Eds. Berlin, Germany: Springer, 2011, pp. 243–252.

[5]

G. Yu, T. Vladimirova, and M. N. Sweeting, “Image compression systems on board satellites,” Acta Astronautica, vol. 64, pp. 988–1005, May/Jun. 2009.

[6]

M. J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS,” IEEE Trans. Image Process., vol. 9, no. 8, pp. 1309–1324, Aug. 2000.

Digital Library

[7]

X. Wu and N. Memon, “Context-based, adaptive, lossless image coding,” IEEE Trans. Commun., vol. 45, no. 4, pp. 437–444, Apr. 1997.

[8]

High efficiency video coding, International Organization for Standardization, document ISO/IEC 23008-2, ITU-T Rec. H.265, Dec. 2013.

[9]

G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the high efficiency video coding (HEVC) standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649–1668, Dec. 2012.

Digital Library

[10]

M. Zhou, W. Gao, M. Jiang, and H. Yu, “HEVC lossless coding and improvements,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1839–1843, Dec. 2012.

Digital Library

[11]

J. Y. Cheong and I. K. Park, “Deep CNN-based super-resolution using external and internal examples,” IEEE Signal Process. Lett., vol. 24, no. 8, pp. 1252–1256, Aug. 2017.

[12]

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Proc. Int. Conf. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, vol. 1, 2012, pp. 341–349.

[13]

D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in Proc. Adv. Neural Inf. Process. Syst., Montreal, QC, Canada, Dec. 2014, pp. 2366–2374. [Online]. Available: https://papers.nips.cc/paper/5539-depth-map-prediction-from-a-single-image-using-a-multi-scale-deep-network

[14]

F. Liu, S. Chunhua, and L. Guosheng, “Deep convolutional neural fields for depth estimation from a single image,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, 2015, pp. 5162–5170. [Online]. Available: https://ieeexplore.ieee.org/document/7299152

[15]

N. K. Kalantari, T.-C. Wang, and R. Ramamoorthi, “Learning-based view synthesis for light field cameras,” ACM Trans. Graph., vol. 35, no. 6, 2016, Art. no. 193.

Digital Library

[16]

G. Todericiet al., “Full resolution image compression with recurrent neural networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 5435–5443.

[17]

J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational image compression with a scale hyperprior,” in Proc. Int. Conf. Learn. Represent. (ICMR), Vancouver, BC, Canada, 2018, pp. 1–23.

[18]

J. Li, B. Li, J. Xu, R. Xiong, and W. Gao, “Fully connected network-based intra prediction for image coding,” IEEE Trans. Image Process., vol. 27, no. 7, pp. 3236–3247, Jul. 2018.

[19]

I. Schiopu, Y. Liu, and A. Munteanu, “CNN-based prediction for lossless coding of photographic images,” in Proc. Picture Coding Symp. (PCS), San Francisco, CA, USA, Jun. 2018, pp. 16–20.

[20]

I. Schiopu and A. Munteanu, “Residual-error prediction based on deep learning for lossless image compression,” Electron. Lett., vol. 54, no. 17, pp. 1032–1034, Aug. 2018.

[21]

I. Schiopu and A. Munteanu, “Macro-pixel prediction based on convolutional neural networks for lossless compression of light field images,” in Proc. 25th IEEE Int. Conf. Image Process. (ICIP), Athens, Greece, Oct. 2018, pp. 445–449.

[22]

Digital Compression and Coding of Continuous Tone Still Images—Requirements and Guidelines, Standard ITU Rec. T.81, ISO/IEC 10918-1, Sep. 1993.

[23]

J. Sneyers and P. Wuille, “FLIF: Free lossless image format based on MANIAC compression,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Phoenix, AZ, USA, Sep. 2016, pp. 66–70.

[24]

J. Sneyers and P. Wuille FLIF Website. [Online]. Available: https://flif. info

[25]

G. Lippmann, “Épreuves réversibles donnant la sensation du relief,” J. Phys., vol. 7, no. 4, pp. 821–825, 1908.

[26]

E. H. Adelson and J. Y. A. Wang, “Single lens stereo with a plenoptic camera,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 99–106, Feb. 1992.

Digital Library

[27]

R. Ng, M. Levoy, M. Brédif, G. Duval, M. Horowitz, and P. Hanrahan, “Light field photography with a hand-held plenoptic camera,” Dept. Comput. Sci., Stanford Univ., Stanford, CA, USA, 2005, pp. 1–11.

[28]

A. Lumsdaine and T. Georgiev, “The focused plenoptic camera,” in Proc. IEEE Int. Conf. Comput. Photography, San Francisco, CA, USA, Apr. 2009, pp. 1–8.

[29]

C. Perwaß and L. Wietzke, “Single lens 3D-camera with extended depth-of-field,” Proc. SPIE, vol. 8291, pp. 829108-1–829108-15, Feb. 2012. [Online]. Available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8291/829108/Single-lens-3D-camera-with-extended-dep th-of-field/10.1117/12.909882

[30]

C. Perra, “Lossless plenoptic image compression using adaptive block differential prediction,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Brisbane, QLD, Australia, Apr. 2015, pp. 1231–1234.

[31]

I. Schiopu, M. Gabbouj, A. Gotchev, and M. M. Hannuksela, “Lossless compression of subaperture images using context modeling,” in Proc. 3DTV Conf., True Vis.-Capture, Transmiss. Display 3D Video, Copenhagen, Denmark, Jun. 2017, pp. 1–4.

[32]

P. Helin, P. Astola, B. Rao, and I. Tabus, “Minimum description length sparse modeling and region merging for lossless plenoptic image compression,” IEEE J. Sel. Topics Signal Process., vol. 11, no. 7, pp. 1146–1161, Oct. 2017.

[33]

J. M. Santos, P. A. A. Assuncao, L. A. da Silva Cruz, L. Távora, R. Fonseca-Pinto, and S. M. M. Faria, “Lossless light-field compression using reversible colour transformations,” in Proc. 7th Int. Conf. Image Process. Theory, Tools Appl. (IPTA), Montreal, QC, Canada, Nov./Dec. 2017, pp. 1–6.

[34]

C. Conti, J. Lino, P. Nunes, L. D. Soares, and P. L. Correia, “Improved spatial prediction for 3D holoscopic image and video coding,” in Proc. 19th Eur. Signal Process. Conf., Barcelona, Spain, Aug./Sep. 2011, pp. 378–382.

[35]

C. Perra and P. Assuncao, “High efficiency coding of light field images based on tiling and pseudo-temporal data arrangement,” in Proc. IEEE Int. Conf. Multimedia Expo Workshops, Seattle, WA, USA, Jul. 2016, pp. 1–4.

[36]

D. Liu, L. Wang, L. Li, Z. Xiong, F. Wu, and W. Zeng, “Pseudo-sequence-based light field image compression,” in Proc. IEEE Int. Conf. Multimedia Expo Workshops, Seattle, WA, USA, Jul. 2016, pp. 1–4.

[37]

L. Li, Z. Li, B. Li, D. Liu, and H. Li, “Pseudo sequence based 2-D hierarchical coding structure for light-field image compression,” in Proc. Data Compress. Conf., Snowbird, UT, USA, Apr. 2017, pp. 131–140.

[38]

T. Ebrahimi, P. Schelkens, and F. Pereira ICME 2016 Grand Challenge: Light-Field Image Compression. Accessed: Mar. 1, 2017. [Online]. Available: https://mmspg.epfl.ch/meetings/page-71686-en-html/icme2016grandchallenge_1/

[39]

T. Ebrahimi, F. Pereira, P. Schelkens, and S. Foessela, Grand Challenges: Light Field Image Coding. Accessed: Mar. 1, 2017. [Online]. Available: http://www.2017.ieeeicip.org/GrandChallenges.html

[40]

R. Zhong, I. Schiopu, B. Cornelis, S.-P. Lu, J. Yuan, and A. Munteanu, “Dictionary learning-based, directional, and optimized prediction for lenslet image coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 4, pp. 1116–1129, Apr. 2019.

[41]

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. Int. Conf. Mach. Learn., Lille, France, Feb. 2015, pp. 448–456. [Online]. Available: https://dl.acm.org/citation.cfm?id=3045118.3045167

[42]

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proc. 13th Int. Conf. Artif. Intell. Statist., Sardinia, Italy, May 2010, pp. 249–256.

[43]

K. He, X. Zhang, S. Ren, and J. Sun (Feb. 2015). “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.” [Online]. Available: https://arxiv.org/abs/1502.01852

Digital Library

[44]

F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi, “Learning activation functions to improve deep neural networks,” CoRR, vol. abs/1412.6830, pp. 1–9, Dec. 2014. [Online]. Available: https://arxiv.org/abs/1412.6830

[45]

V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. 27th Int. Conf. Int. Conf. Mach. Learn. (ICML), Washington, DC, USA, 2010, pp. 807–814.

[46]

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. Int. Conf. Mach. Learn. (ICML), Atlanta, GA, USA, 2013, pp. 1–3.

[47]

D. P. Kingma and J. Ba (Dec. 2014). “Adam: A method for stochastic optimization.” [Online]. Available: https://arxiv.org/abs/1412.6980

[48]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778. [Online]. Available: https://ieeexplore.ieee.org/document/7780459

[49]

C. Szegedyet al., “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, 2015, pp. 1–9. [Online]. Available: https://ieeexplore.ieee.org/document/7298594

[50]

I. J. Goodfellowet al., “Generative adversarial networks,” in Proc. Adv. Neural Inf. Process. Syst., Montreal, QC, Canada, Jun. 2014, pp. 2672–2680. [Online]. Available: https://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014

[51]

A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu (Jun. 2016). “Conditional image generation with PixelCNN decoders.” [Online]. Available: https://arxiv.org/abs/1606.05328

[52]

I. Schiopu, “Depth-map image compression based on region and contour modeling,” M.S. thesis, Tampere Univ. Technol., Tampere, Finland, Jan. 2016, vol. 1360. [Online]. Available: https://tutcris.tut.fi/portal/en/publications/depthmap-image-compression-based-on-region-an d-contour-modeling(913b8afb-a1ad-44df-b399-61c7756d5ef9)/export.html

[53]

R. Krichevsky and V. Trofimov, “The performance of universal encoding,” IEEE Trans. Inf. Theory, vol. 27, no. 2, pp. 199–207, Mar. 1981.

Digital Library

[54]

4K UHD Photographic Images. Accessed: Aug. 25, 2017. [Online]. Available: http://www.ultrahdwallpapers.net/nature

[55]

M. Rerabek and T. Ebrahimi, “New light field image dataset,” in Proc. 8th Int. Conf. Qual. Multimedia Exper., Lisbon, Portugal, 2016, pp. 1–2.

[56]

JPEG Pleno Database: EPFL Light-Field Data Set. Accessed: Mar. 1, 2017. [Online]. Available: https://jpeg.org/plenodb/lf/epfl

[57]

Ultra Video Group. Tampere University of Technology. Test Sequences. Accessed: Jul. 1, 2018. [Online]. Available: http://ultravideo.cs.tut.fi/#testsequences

[58]

Nvidia. Titan X Specifications. Accessed: Sep. 1, 2018. [Online]. Available: https://www.nvidia.com/en-us/geforce/products/10series/titan-x-pascal

[59]

Xiph.Org Foundation. Video Test Media. Accessed: Jul. 1, 2018. [Online]. Available: https://media.xiph.org/video/derf

[60]

MulticoreWare. x265 Source Code, Version 2.7. Accessed: May 4, 2018. [Online]. Available: https://bitbucket.org/multicoreware/x265/downloads

Cited By

Liu XWang MWang SKwong S(2024)Bilateral Context Modeling for Residual Coding in Lossless 3D Medical Image CompressionIEEE Transactions on Image Processing10.1109/TIP.2024.337891033(2502-2513)Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3378910
Bourai NMerouani HDjebbar A(2024)Deep learning-assisted medical image compression challenges and opportunities: systematic reviewNeural Computing and Applications10.1007/s00521-024-09660-836:17(10067-10108)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09660-8
Xue DMa HLi LLiu DXiong ZLi H(2023)DBVC: An End-to-End 3-D Deep Biomedical Video Coding FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.330322834:4(2922-2933)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3303228
Show More Cited By

Index Terms

Deep-Learning-Based Lossless Image Coding
1. Computing methodologies

Index terms have been assigned to the content through auto-classification.

Recommendations

HEVC-based lossless intra coding for efficient still image compression

Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint ...
Lossless-by-Lossy Coding for Scalable Lossless Image Compression

This paper presents a method of scalable lossless image compression by means of lossy coding. A progressive decoding capability and a full decoding for the lossless rendition are equipped with the losslessly encoded bit stream. Embedded coding is ...
Lossless Image Compression by PPM-Based Prediction Coding
DCC '09: Proceedings of the 2009 Data Compression Conference

Most of speech and image data compressed by lossy compression whose decompressed data are different from the original ones. Here, the different between the decompressed data and the original ones cannot be recognized by most of people. Lossless image ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 30, Issue 7

July 2020

510 pages

ISSN:1051-8215

Issue’s Table of Contents

1051-8215 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Publisher

IEEE Press

Publication History

Published: 01 July 2020

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu XWang MWang SKwong S(2024)Bilateral Context Modeling for Residual Coding in Lossless 3D Medical Image CompressionIEEE Transactions on Image Processing10.1109/TIP.2024.337891033(2502-2513)Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3378910
Bourai NMerouani HDjebbar A(2024)Deep learning-assisted medical image compression challenges and opportunities: systematic reviewNeural Computing and Applications10.1007/s00521-024-09660-836:17(10067-10108)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09660-8
Xue DMa HLi LLiu DXiong ZLi H(2023)DBVC: An End-to-End 3-D Deep Biomedical Video Coding FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.330322834:4(2922-2933)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3303228
Kamisli F(2023)Learned Lossless Image Compression Through Interpolation With Low ComplexityIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327357833:12(7832-7841)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3273578
Li LYao YYu N(2023)High-fidelity video reversible data hiding using joint spatial and temporal predictionSignal Processing10.1016/j.sigpro.2023.108970208:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.sigpro.2023.108970
Dardouri TKaaniche MBenazza-Benyahia APesquet J(2022)Dynamic Neural Network for Lossy-to-Lossless Image CodingIEEE Transactions on Image Processing10.1109/TIP.2021.313282531(569-584)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TIP.2021.3132825
Li FLi WGao XLiu RXiao B(2022)DCNetKnowledge-Based Systems10.1016/j.knosys.2022.110033258:COnline publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.110033
Sakurai TInoue U(2021)Lossless Image Set Compression Using Animated FLIFProceedings of the the 8th International Virtual Conference on Applied Computing & Information Technology10.1145/3468081.3471130(99-104)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3468081.3471130
Fan CLi FJiao YLiu X(2021)A novel lossless compression framework for facial depth images in expression recognitionMultimedia Tools and Applications10.1007/s11042-021-10796-180:16(24173-24183)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s11042-021-10796-1

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents