High Efficiency Video Coding Intra Prediction Optimization Algorithm Based on Region of Interest
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摘要: 针对高性能视频编码(HEVC)帧内预测编码算法复杂度较高的问题，该文提出一种基于感兴趣区域的高性能视频编码帧内预测优化算法。首先，根据图像显著性划分当前帧的感兴趣区域(ROI)和非感兴趣区域(NROI)；然后，对ROI基于空域相关性采用提出的快速编码单元(CU)划分算法决定当前编码单元的最终划分深度，跳过不必要的CU划分过程；最后，基于ROI采用提出的预测单元(PU)模式快速选择算法计算当前PU的能量和方向，根据能量和方向确定当前PU的预测模式，减少率失真代价的相关计算，达到降低编码复杂度和节省编码时间的目的。实验结果表明，在峰值信噪比(PSNR)损失仅为0.0390 dB的情况下，所提算法可以平均降低47.37%的编码时间。Abstract: For the high complexity of High Efficiency Video Coding (HEVC) intra prediction coding algorithm, an HEVC intra prediction optimization algorithm based on Region Of Interest (ROI) is proposed. Firstly, the algorithm divides the Region Of Interest and Non-Region Of Interest (NROI) of the current frame according to image saliency; Then, the final grading depth of the current coding unit is determined by the proposed fast Coding Unit (CU) partitioning algorithm based on spatial correlation in the ROI, and the unnecessary CU partitioning process is skipped. Finally, the proposed Prediction Unit (PU) mode fast selection algorithm is used to calculate the energy and direction of the current PU based on the ROI, and the current PU prediction mode is determined according to the energy and direction, and the correlation calculation of the rate distortion cost is reduced, Achieving the purposes of reducing coding complexity and saving coding time. The experimental results show that the proposed algorithm can reduce the coding time by 47.37% on average when the Peak Signal-to-Noise Ratio (PSNR) loss is only 0.0390 dB.
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表 1 快速CU划分算法正确率和PU预测模式快速选择算法命中率(%)
序列 QP=22 QP=27 QP=32 QP=37 平均 Traffic 93.7/91.4 95.6/92.3 96.1/95.6 96.8/96.1 95.6/93.9 BQTerrace 93.1/89.7 94.8/91.4 95.8/93.5 96.4/94.7 95.0/92.3 Partyscene 92.4/90.2 94.7/93.1 95.6/93.9 96.2/94.8 94.7/93.0 Blowing Bubbles 91.1/88.6 93.4/90.3 94.7/92.5 95.8/93.7 93.8/91.3 Johnny 92.3/89.8 94.6/92.7 95.3/94.5 96.1/95.3 94.6/93.1 平均 92.5/89.9 94.6/91.9 95.5/94.0 96.3/94.9 94.7/92.7 
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分辨率 序列 BDBR(%) BDPSNR(dB) $T$(%) $2560 \times 1600$ Traffic 0.7054/0.6874/0.6013 –0.0406/–0.0396/–0.0327 42.19/43.62/46.89 PeopleOnStreet 1.2017/1.1047/0.7161 –0.0593/–0.0617/–0.0410 43.94/45.05/50.14 $1920 \times 1080$ Kimono 0.6725/0.6435/0.6314 –0.0351/–0.0309/–0.0293 42.76/43.93/47.93 Basketball Drive 1.3316/1.2704/1.0341 –0.0296/–0.0311/–0.0274 43.35/44.86/48.19 Cactus 1.2073/1.3160/0.9758 –0.0314/–0.0348/–0.0317 41.87/45.16/48.34 $832 \times 480$ BQMall 1.1986/1.1476/0.7692 –0.0724/–0.0769/–0.0405 40.01/42.93/45.54 Basketball Drill 1.3843/1.2543/0.6963 –0.0716/–0.0683/–0.0317 39.16/43.47/46.74 RaceHorsesC 1.2196/1.1702/0.7163 –0.0631/–0.0574/–0.0385 40.54/43.24/45.83 $416 \times 240$ Keiba 1.4055/1.1394/0.5631 –0.0965/–0.0846/–0.0417 41.96/43.56/46.14 BQSquare 1.3423/1.2761/0.6176 –0.0913/–0.0877/–0.0475 41.64/44.87/46.86 BasketballPass 1.4063/1.4322/0.7568 –0.0714/–0.0793/–0.0513 43.45/44.14/47.43 $1280 \times 720$ FourPeople 0.9704/0.9417/0.6975 –0.0542/–0.0523/–0.0372 42.64/43.17/47.39 Vidy01 0.6725/0.6524/0.7351 –0.0403/–0.0443/–0.0462 41.47/41.83/46.87 Vidyo3 1.0457/0.9125/0.8143 –0.0562/–0.0549/–0.0496 42.09/42.54/46.13 平均 1.1260/1.0677/0.7375 –0.0581/–0.0574/–0.0390 41.93/43.74/47.17
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表 3 本文算法与文献[13]算法实验结果对比
Class 文献[13]算法 本文算法 BDBR(%) BDPSNR(dB) $T$(%) BDBR(%) BDPSNR(dB) $T$(%) ClassA 0.9236 –0.0742 44.19 0.6697 –0.0392 48.62 ClassB 1.1747 –0.0557 45.77 0.8926 –0.0327 48.74 ClassC 1.3532 –0.0823 41.89 0.7369 –0.0354 45.86 ClassD 1.3479 –0.1022 43.94 0.6461 –0.0473 46.69 ClassE 1.0754 –0.0837 43.76 0.7493 –0.0441 46.93 平均 1.1750 –0.0796 43.91 0.7389 –0.0397 47.37
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