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一种方向插值预测变长编码的帧存有损压缩算法

罗瑜 张珍珍

罗瑜, 张珍珍. 一种方向插值预测变长编码的帧存有损压缩算法[J]. 电子与信息学报, 2019, 41(10): 2495-2500. doi: 10.11999/JEIT181195
引用本文: 罗瑜, 张珍珍. 一种方向插值预测变长编码的帧存有损压缩算法[J]. 电子与信息学报, 2019, 41(10): 2495-2500. doi: 10.11999/JEIT181195
Yu LUO, Zhenzhen ZHANG. A Lossy Frame Memory Compression Algorithm Using Directional Interpolation Prediction Variable Length Coding[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2495-2500. doi: 10.11999/JEIT181195
Citation: Yu LUO, Zhenzhen ZHANG. A Lossy Frame Memory Compression Algorithm Using Directional Interpolation Prediction Variable Length Coding[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2495-2500. doi: 10.11999/JEIT181195

一种方向插值预测变长编码的帧存有损压缩算法

doi: 10.11999/JEIT181195
基金项目: 国家高技术研究发展计划(863计划)(2015M16903)
详细信息
    作者简介:

    罗瑜:女,1984年生,副教授,研究方向为图形图像处理

    张珍珍:女,1984年生,博士生,研究方向图形图像处理

    通讯作者:

    罗瑜 luoyu2010@163.com

  • 中图分类号: TN919.81

A Lossy Frame Memory Compression Algorithm Using Directional Interpolation Prediction Variable Length Coding

Funds: The National High Technology Research and Development Program of China (2015M16903)
  • 摘要: 为了提高帧存储的压缩性能,该文提出一种基于方向插值预测变长编码(DIPVLC)的帧存有损压缩算法。首先根据自适应纹理方向插值获取参考像素,从而得到预测残差,然后优化率失真模型对预测残差进行量化,最后通过游程哥伦布算法对量化残差进行变长编码。实验结果显示,与内容感知自适应量化(CAAQ)的帧存压缩算法相比,该文算法不但PSNR下降更少,而且压缩率提高了10.05%,同时编码时间减少了10.62%。
  • 图  1  自适应预测坐标示意图

    图  2  方向插值预测图

    图  3  游程哥伦布编码图

    图  4  thr测试对比

    表  1  哥伦布商码表

    QRk=0k=1k=2k=3
    00000000000
    ±110010010001
    ±21101000100010
    ±311101010110100
    ±4111100110010000101
    ±5111101110110010110
    $\vdots $$\vdots $$\vdots $$\vdots $$\vdots $
    下载: 导出CSV

    表  2  哥伦布商码表

    QRk=0k=1k=2k=3
    00000000000
    ±110010010001
    ±21101000100010
    ±311101010110011
    ±41111*110010000100
    ±51101*10010110
    ±61010
    ±71011*$ \vdots $
    $ \vdots $
    $ \vdots $
    ±151111*
    下载: 导出CSV

    表  3  本文算法模块性能提升对比

    序列CR(%)${\rm{\Delta }} {\rm{PSNR(dB)}}$RET 模块/CAAQ(%)
    CAAQ预测率失真编码CAAQ预测率失真编码预测率失真编码
    bluesky80.2683.6985.6486.25–0.05–0.05–0.04–0.0499.5698.1585.34
    traffic70.9573.1177.6476.61–0.07–0.06–0.03–0.0599.12100.5490.12
    riverbed60.2161.2167.2165.21–0.09–0.09–0.04–0.0598.89101.5195.14
    平均70.4772.6776.8376.02–0.07–0.07–0.04–0.0599.19100.0790.20
    下载: 导出CSV

    表  4  本文算法与CAAQ算法压缩的性能对比

    序列CR(%)${\rm{\Delta }} {\rm{PSNR(dB)}}$RET
    CAAQ本文算法CAAQ本文算法本文/CAAQ(%)
    Tennis78.2193.23–0.02–70.0186.00
    bluesky80.2691.45–0.05–0.0385.21
    Johnny81.3993.56–0.05–0.0284.25
    crowdrun71.2179.56–0.06–0.0189.15
    traffic70.9582.10–0.07–0.0289.25
    stockholm70.1279.12–0.08–0.0388.56
    racehorses64.3673.14–0.06–0.0192.31
    riverbed60.2168.52–0.09–0.0296.14
    mobcal59.7667.14–0.08–0.0393.54
    平均70.7280.87–0.06–0.0289.38
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-03
  • 修回日期:  2019-05-20
  • 网络出版日期:  2019-05-29
  • 刊出日期:  2019-10-01

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