High Efficiency Video Coding Intra Prediction Optimization Algorithm Based on Region of Interest
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School of Computer, Northeast Electric Power University, Jilin 132012, China
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摘要: 針對高性能視頻編碼(HEVC)幀內(nèi)預測編碼算法復雜度較高的問題，該文提出一種基于感興趣區(qū)域的高性能視頻編碼幀內(nèi)預測優(yōu)化算法。首先，根據(jù)圖像顯著性劃分當前幀的感興趣區(qū)域(ROI)和非感興趣區(qū)域(NROI)；然后，對ROI基于空域相關性采用提出的快速編碼單元(CU)劃分算法決定當前編碼單元的最終劃分深度，跳過不必要的CU劃分過程；最后，基于ROI采用提出的預測單元(PU)模式快速選擇算法計算當前PU的能量和方向，根據(jù)能量和方向確定當前PU的預測模式，減少率失真代價的相關計算，達到降低編碼復雜度和節(jié)省編碼時間的目的。實驗結果表明，在峰值信噪比(PSNR)損失僅為0.0390 dB的情況下，所提算法可以平均降低47.37%的編碼時間。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劃分算法正確率和PU預測模式快速選擇算法命中率(%)
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 本文算法與文獻[13]算法實驗結果對比
Class 文獻[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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