An Optimized Algorithm at CTU-level for Rate Control
Authors: 
Zeng Li, Ruidong Fang, Zhengteng Zhang, Yuhan Cai, Yapeng Du, Jucai Lin, Jun YinAuthors Info & Claims
Zeng Li, Ruidong Fang, Zhengteng Zhang, Yuhan Cai, Yapeng Du, Jucai Lin, Jun YinAuthors Info & ClaimsPages 33 - 40
Abstract
CTU-level bit allocation in the High Efficiency Video Coding (HEVC) standard does not fully consider the texture characteristics of related CTUs. In order to solve this problem, this paper proposed a CTU-level algorithm for rate control. Firstly, the partition of current CTU is determined by the depth of the spatially co-located CTU in the latest frame. Secondly, the weight of target bitrate for each CTU will be improved according to the ratio of complex and flat blocks in the subCUs, which is obtained by the partition. In addition, considering the texture characteristics of different blocks in current CTU, this paper also adjusts the QP value of each coding unit (CU) according to the bit per pixel (bpp) and SATD per pixel (spp). The experimental results show that compared with the CTU-level rate control algorithm in the test model HM16.20, the error rate of the proposed algorithm in the LDP, LDB, and RA configuration is reduced by 0.72%, 0.74%, and 2.33%, respectively. And the subjective quality of the videos are improved while the volatility of rate is basically unchanged.
References
[1]
G. Sullivan, J. Ohm, W. Han, “Overview of the high efficiency video coding (HEVC) standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649–1668, Dec. 2012.
[2]
B. Li, H. Q. Li, L. Li, “λ domain rate control algorithm for high efficiency video coding,” IEEE Trans. Image Process., vol. 23, no. 9, pp. 3841-3854, Sept. 2014.
[3]
Y. Song, K. Jia and Z. Wei. “Improved LCU Level Rate Control for 3D-HEVC,” in 2016 Visual Communications and Image Processing, Chengdu, 2016, pp.1-4.
[4]
Z. Cui, M. Zhang, X. Jiang, “Improving HEVC Coding Perceptual Quality Using Saliency Guided CTU Layer QP Adjustment,” in 2019 Chinese Automation Congress, Hangzhou, 2019, pp. 5524-5529.
[5]
W Lim, D Sim. “A perceptual rate control algorithm based on luminance adaptation for HEVC encoders,” Signal, Image and Video Processing, vol. 14, no. 1, pp. 887–895, Jan. 2020.
[6]
S. Wang, J. Li1, S. Wang, “A Frame Level Rate Control Algorithm for Screen Content Coding,” 2018 IEEE International Symposium on Circuits and Systems, Florence, 2018, pp. 1-4.
[7]
X. Liu, Y. Zhang, L. Zhu, "Perception-Based CTU Level Bit Allocation for Intra High Efficiency Video Coding," in IEEE Access, vol. 7, pp. 154959-154970, 2019. 2019.2948709.
[8]
M. Wang, K. N. Ngan and H. Li. "Low-Delay Rate Control for Consistent Quality Using Distortion-Based Lagrange Multiplier," in IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 2943-2955, July 2016.
[9]
B. Li, H. Li, L. Li. Adaptive bit allocation for R-lambda model rate control in HM. Incheon, Korea: Document JCTVC-M0036, April, 2013.
[10]
F. Bossen. Common test conditions and software reference configurations. Geneva, Switzerland: Document JCTVC-L1100, January, 2013.
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Published In
May 2021
293 pages
ISBN:9781450389655
DOI:10.1145/3473258
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Association for Computing Machinery
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Published: 11 December 2021
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ICBBT '21
ICBBT '21: 2021 13th International Conference on Bioinformatics and Biomedical Technology
May 21 - 23, 2021
Xi'an, China
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