Increased Compression Efficiency of AVC and HEVC CABAC by Precise Statistics Estimation

• Autorzy: Karwowski D. , Domański M.

Identyfikatory

DOI

10.24425/123519

• Języki publikacji: EN

Abstrakty

EN

The paper presents Improved Adaptive Arithmetic Coding algorithm for application in future video compression technology. The proposed solution is based on the Context-based Adaptive Binary Arithmetic Coding (CABAC) technique and uses the authors mechanism of symbols probability estimation that exploits Context-Tree Weighting (CTW) technique. This paper proposes the version of the algorithm, that allows an arbitrary selection of depth D of context trees, when activating the algorithm in the framework of the AVC or HEVC video encoders. The algorithm has been tested in terms of coding efficiency of data and its computational complexity. Results showed, that depending on depth of context trees from 0.1% to 0.86% reduction of bitrate is achieved, when using the algorithm in the HEVC video encoder and 0.4% to 2.3% compression gain in the case of the AVC. The new solution increases complexity of entropy encoder itself, however, this does not cause an increase of the complexity of the whole video encoder.

Słowa kluczowe

EN

entropy coding; CABAC algorithm; CTW technique; Video Compression; AVC; HEVC

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2018
• Tom: Vol. 64, No. 3
• Strony: 277--284
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Karwowski D.

• Chair of Multimedia Telecommunications and Microelectronics, Poznan University of Technology, Poland

autor

Domański M.

• Chair of Multimedia Telecommunications and Microelectronics, Poznan University of Technology, Poland

Bibliografia

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• [3] ISO/IEC 14496-10, Generic Coding of Audio-Visual Objects, Part 10: Advanced Video Coding, March 2006.
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• [17] X. Tian, T. M. Le, Y. Lian, Entropy Coders of the H.264/AVC Standard, Springer-Verlag, 2011.
• [18] F. M. J. Willems, Y. M. Shtarkov, and Tj. J. Tjalkens, The Context-Tree Weighting Method: Basic Properties, IEEE Transactions on Information Theory, Vol. 41, No. 3, pp. 653-664, May 1995.
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• [22] G. Bjntegaard, Calculation of Average PSNR Differences between RD curves, ITU-T SG16/Q6, 13th VCEG Meeting, Austin, Texas, USA, April 2001, Doc. VCEG-M33.
• [23] A. Alshin, E. Alshina, J. Park, High precision probability estimation for CABAC, Visual Communications and Image Processing (VCIP) 2013, pp. 1-6.
• [24] D. Karwowski, M. Domański, Context-Adaptive Binary Arithmetic Coding with Precise Probability Estimation and Complexity Scalability for High-Efficiency Video Coding, Journal of Electronic Imaging, 25(1), 013010 (January 20, 2016); DOI: 10.1117/1.JEI.25.1.013010.
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Uwagi

1. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).

2. The work was supported by National Science Centre, Poland, according to the decision DEC-2012/05/B/ST7/01279.