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基于感兴趣区域的高性能视频编码帧内预测优化算法

宋人杰 张元东

引用本文: 宋人杰, 张元东. 基于感兴趣区域的高性能视频编码帧内预测优化算法[J]. 电子与信息学报, doi: 10.11999/JEIT190330 shu
Citation:  Renjie SONG, Yuandong ZHANG. High Efficiency Video Coding Intra Prediction Optimization Algorithm Based on Region of Interest[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190330 shu

基于感兴趣区域的高性能视频编码帧内预测优化算法

    作者简介: 宋人杰: 女,1966年生,教授,研究方向为数字图像处理与可视化应用、计算机视觉与电力应用的研究;
    张元东: 男,1993年生,硕士生,研究方向为感兴趣区域HEVC算法的研究
    通讯作者: 张元东,1406632033@qq.com
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。

English

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  • 图 1  本文算法与文献[8]、文献[9]算法的检测结果

    图 2  本文算法和HM13.0算法的RD性能比较

    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=22QP=27QP=32QP=37平均
    Traffic93.7/91.495.6/92.396.1/95.696.8/96.195.6/93.9
    BQTerrace93.1/89.794.8/91.495.8/93.596.4/94.795.0/92.3
    Partyscene92.4/90.294.7/93.195.6/93.996.2/94.894.7/93.0
    Blowing Bubbles91.1/88.693.4/90.394.7/92.595.8/93.793.8/91.3
    Johnny92.3/89.894.6/92.795.3/94.596.1/95.394.6/93.1
    平均92.5/89.994.6/91.995.5/94.096.3/94.994.7/92.7
    下载: 导出CSV

    表 2  本文算法与文献[3]算法及文献[6]算法实验结果对比

    分辨率序列BDBR(%)BDPSNR(dB)$T$(%)
    $2560 \times 1600$Traffic0.7054/0.6874/0.6013–0.0406/–0.0396/–0.032742.19/43.62/46.89
    PeopleOnStreet1.2017/1.1047/0.7161–0.0593/–0.0617/–0.041043.94/45.05/50.14
    $1920 \times 1080$Kimono0.6725/0.6435/0.6314–0.0351/–0.0309/–0.029342.76/43.93/47.93
    Basketball Drive1.3316/1.2704/1.0341–0.0296/–0.0311/–0.027443.35/44.86/48.19
    Cactus1.2073/1.3160/0.9758–0.0314/–0.0348/–0.031741.87/45.16/48.34
    $832 \times 480$BQMall1.1986/1.1476/0.7692–0.0724/–0.0769/–0.040540.01/42.93/45.54
    Basketball Drill1.3843/1.2543/0.6963–0.0716/–0.0683/–0.031739.16/43.47/46.74
    RaceHorsesC1.2196/1.1702/0.7163–0.0631/–0.0574/–0.038540.54/43.24/45.83
    $416 \times 240$Keiba1.4055/1.1394/0.5631–0.0965/–0.0846/–0.041741.96/43.56/46.14
    BQSquare1.3423/1.2761/0.6176–0.0913/–0.0877/–0.047541.64/44.87/46.86
    BasketballPass1.4063/1.4322/0.7568–0.0714/–0.0793/–0.051343.45/44.14/47.43
    $1280 \times 720$FourPeople0.9704/0.9417/0.6975–0.0542/–0.0523/–0.037242.64/43.17/47.39
    Vidy010.6725/0.6524/0.7351–0.0403/–0.0443/–0.046241.47/41.83/46.87
    Vidyo31.0457/0.9125/0.8143–0.0562/–0.0549/–0.049642.09/42.54/46.13
    平均1.1260/1.0677/0.7375–0.0581/–0.0574/–0.039041.93/43.74/47.17
    下载: 导出CSV

    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)$T$(%)
    ClassA0.9236–0.074244.190.6697–0.039248.62
    ClassB1.1747–0.055745.770.8926–0.032748.74
    ClassC1.3532–0.082341.890.7369–0.035445.86
    ClassD1.3479–0.102243.940.6461–0.047346.69
    ClassE1.0754–0.083743.760.7493–0.044146.93
    平均1.1750–0.079643.910.7389–0.039747.37
    下载: 导出CSV
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文章相关
  • 通讯作者:  张元东, 1406632033@qq.com
  • 收稿日期:  2019-05-13
  • 录用日期:  2020-05-24
  • 网络出版日期:  2020-07-01
通讯作者: 陈斌, bchen63@163.com
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