低复杂度的HEVC帧内编码模式决策算法
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摘要
新一代视频编码标准HEVC虽然显著提升了视频压缩效率,但也大幅增加了视频编码的计算复杂度,其中模式决策部分消耗的编码时间最多.为了降低HEVC编码的计算复杂度,提出一种基于纹理划分特征和方向特征的低复杂度帧内编码模式决策算法.首先根据编码树单元(CTU)的纹理划分特征与最佳编码单元(CU)划分的相关性,通过分析CTU中所有16×16CU的纹理划分特征,自底向上计算不同尺寸CU的纹理划分标识;然后利用这些标识预测当前CTU的深度范围,以及判定是否提前终止CU划分;接着根据预测单元(PU)纹理方向特征与最佳帧内预测模式的相关性,对候选帧内预测模式进行两级选择,以减少进行哈达玛优化的预测模式个数;最后利用哈达玛代价减少进行率失真优化的预测模式个数.实验结果表明,本文算法与HEVC参考模型相比,能够平均降低49.72%的编码时间,而码率只增加0.59%、峰值信噪比仅下降0.04d B,保持了良好的编码率失真性能;与现有的两种模式决策快速算法相比,本文算法进一步降低了约8%和9%的编码时间,并具有相近的编码率失真性能.
High efficiency video coding( HEVC) is the newgeneration of video coding standard,which can promote the video compression efficiency significantly. However,the HEVC coding has enormous computational complexity,and the part of mode decision consumes most of the time. In this paper,a lowcomplexity mode decision algorithm based on textural division and direction features is proposed for HEVC intra coding. First,according to the correlation between the textural division feature of Coding Tree Unit( CTU)and the best division of Coding Unit( CU),the textural division features of 16 × 16 CUs in CTU are analyzed,and the textural division flags of different size CUs are calculated from bottom to top. These division flags are utilized to predict the depth range of the current CTU and decide whether to terminate the division of CU. Then,according to the correlation between the textural direction feature and the best intra prediction mode of Prediction Unit( PU),the candidate intra prediction modes are chosen by a two-stage selection method to reduce the number of prediction mode for hadamard optimization. Finally,the hadamard costs are used to reduce the number of candidate prediction modes for rate-distortion optimization. Compared to the HEVC test model,experimental results showthat the proposed algorithm saves 49. 72% encoding time on average with 0. 59% of bitrate increment and 0. 04 d B loss of PSNR,which maintains a good rate-distortion performance. As compared with two state-of-art algorithms,the proposed algorithm achieves about 8% and 9%time savings with a same level rate-distortion performance.
High efficiency video coding( HEVC) is the newgeneration of video coding standard,which can promote the video compression efficiency significantly. However,the HEVC coding has enormous computational complexity,and the part of mode decision consumes most of the time. In this paper,a lowcomplexity mode decision algorithm based on textural division and direction features is proposed for HEVC intra coding. First,according to the correlation between the textural division feature of Coding Tree Unit( CTU)and the best division of Coding Unit( CU),the textural division features of 16 × 16 CUs in CTU are analyzed,and the textural division flags of different size CUs are calculated from bottom to top. These division flags are utilized to predict the depth range of the current CTU and decide whether to terminate the division of CU. Then,according to the correlation between the textural direction feature and the best intra prediction mode of Prediction Unit( PU),the candidate intra prediction modes are chosen by a two-stage selection method to reduce the number of prediction mode for hadamard optimization. Finally,the hadamard costs are used to reduce the number of candidate prediction modes for rate-distortion optimization. Compared to the HEVC test model,experimental results showthat the proposed algorithm saves 49. 72% encoding time on average with 0. 59% of bitrate increment and 0. 04 d B loss of PSNR,which maintains a good rate-distortion performance. As compared with two state-of-art algorithms,the proposed algorithm achieves about 8% and 9%time savings with a same level rate-distortion performance.
引文
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tional Conference on Image Processing,Melbourne,Australia,2013:1578-1582.
[5]Andreas Heindel,Christoph Pylinski,AndreéKaup.Two-stage exclusion of angular intra prediction modes for fast mode decision in HEVC[C].IEEE International Conference on Image Processing,Phoenix,Arizona,USA,2016:529-533.
[6]Kyungmin Lim,Jaeho Lee,Seongwan Kim,et al.Fast PU skip and split termination algorithm for HEVC intra prediction[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(8):1335-1346.
[7]Li Wei,Wang Rang-ding,Wang Jia-ji,et al.A fast intra coding algorithm with lowcomplexity for HEVC[J].Journal of Optoelectronics Laser,2015,26(3):597-604.
[8]Tao Yao-dong,Wang Peng-bo,Gao Chun,et al.A fast coding unit divided decision algorithm for HEVC[J].Journal of Chinese Computer Systems,2015,36(8):1874-1877.
[9]Zhu Shi-ping,Zhang Chun-yan.A fast HEVC intra mode decision algorithm based on machine learning[J].Journal of Optoelectronics Laser,2016,27(11):1199-1207.
[10]Hyo-Song Kim,Rae-Hong Park.Fast CU partitioning algorithm for HEVC using an online-learning based bayesian decision rule[J].IEEE Transactions on Circuits and Systems for Video Technology,2016,26(1):130-138.
[11]Sangsoo Ahn,Bumshik Lee,Munchurl Kim.A novel fast CU encoding scheme based on spatiotemporal encoding parameters for HEVC inter coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(3):422-435.
[12]Frank Bossen.Common test conditions and software reference configurations[C].ITU-T WP3/16 and ISO/IEC JTC1/SC29/WG1112th Meeting,Geneva,2013:1-4.
[13]Gisle Bjontegaard.Calculation of average PSNR differences between RD curves[C].Proceedings of the VCEG-M33,Austin,2001:1-4.
[7]李伟,王让定,王家骥,等.一种低复杂度的HEVC帧内快速编码算法[J].光电子激光,2015,26(3):597-604.
[8]陶耀东,王鹏博,高春,等.一种HEVC编码单元快速划分决策算法[J].小型微型计算机系统,2015,36(8):1874-1877.
[9]祝世平,张春燕.基于机器学习的HEVC帧内模式快速决策算法[J].光电子激光,2016,27(11):1199-1207.
[2]Gary J Sullivan,Jens-Rainer Ohm,Woo-Jin Han,et al.Overviewof the high efficiency video coding(HEVC)standard[J].IEEE Transactions on Circuits and Systems for Video Technology,2012,22(12):1649-1668.
[3]Nan Hu,En-Hui Yang.Fast mode selection for HEVC intra-frame coding with entropy coding refinement based on a transparent composite model[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(9):1521-1532.
[4]Muhammad Usman Karim Khan,Muhammad Shafique,J9rg Henkel,et al.An adaptive complexity reduction scheme with fast prediction unit decision for HEVC intra encoding[C].IEEE Interna-
tional Conference on Image Processing,Melbourne,Australia,2013:1578-1582.
[5]Andreas Heindel,Christoph Pylinski,AndreéKaup.Two-stage exclusion of angular intra prediction modes for fast mode decision in HEVC[C].IEEE International Conference on Image Processing,Phoenix,Arizona,USA,2016:529-533.
[6]Kyungmin Lim,Jaeho Lee,Seongwan Kim,et al.Fast PU skip and split termination algorithm for HEVC intra prediction[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(8):1335-1346.
[7]Li Wei,Wang Rang-ding,Wang Jia-ji,et al.A fast intra coding algorithm with lowcomplexity for HEVC[J].Journal of Optoelectronics Laser,2015,26(3):597-604.
[8]Tao Yao-dong,Wang Peng-bo,Gao Chun,et al.A fast coding unit divided decision algorithm for HEVC[J].Journal of Chinese Computer Systems,2015,36(8):1874-1877.
[9]Zhu Shi-ping,Zhang Chun-yan.A fast HEVC intra mode decision algorithm based on machine learning[J].Journal of Optoelectronics Laser,2016,27(11):1199-1207.
[10]Hyo-Song Kim,Rae-Hong Park.Fast CU partitioning algorithm for HEVC using an online-learning based bayesian decision rule[J].IEEE Transactions on Circuits and Systems for Video Technology,2016,26(1):130-138.
[11]Sangsoo Ahn,Bumshik Lee,Munchurl Kim.A novel fast CU encoding scheme based on spatiotemporal encoding parameters for HEVC inter coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2015,25(3):422-435.
[12]Frank Bossen.Common test conditions and software reference configurations[C].ITU-T WP3/16 and ISO/IEC JTC1/SC29/WG1112th Meeting,Geneva,2013:1-4.
[13]Gisle Bjontegaard.Calculation of average PSNR differences between RD curves[C].Proceedings of the VCEG-M33,Austin,2001:1-4.
[7]李伟,王让定,王家骥,等.一种低复杂度的HEVC帧内快速编码算法[J].光电子激光,2015,26(3):597-604.
[8]陶耀东,王鹏博,高春,等.一种HEVC编码单元快速划分决策算法[J].小型微型计算机系统,2015,36(8):1874-1877.
[9]祝世平,张春燕.基于机器学习的HEVC帧内模式快速决策算法[J].光电子激光,2016,27(11):1199-1207.