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计算机工程

• 图形图像处理 • 上一篇    下一篇

基于小波域和时域的视频质量评价

戴慧慧,桑庆兵   

  1. (江南大学物联网工程学院,江苏无锡214122)
  • 收稿日期:2014-05-30 出版日期:2015-05-15 发布日期:2015-05-15
  • 作者简介:戴慧慧(1989 - ),女,硕士研究生,主研方向:视频质量评价;桑庆兵,副教授、博士。
  • 基金资助:
    国家自然科学基金资助项目“通用无参考图像和视频质量评价方法”(61170120);江苏省自然科学基金资助项目“基于机器 学习与融合集成的无参考图像质量评价模型及应用研究”(BK2011147);江苏省产学研前瞻性联合研究基金资助项目“基于物联网的环 境智能分析与监测系统”(BY2013015-41)。

Video Quality Assessment Based on Wavelet Domain and Temporal Domain

DAI Huihui,SANG Qingbing   

  1. (School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)
  • Received:2014-05-30 Online:2015-05-15 Published:2015-05-15

摘要: 目前多数视频质量评价算法将视频所有帧的图像质量平均值作为整个视频的质量,但该方式只考虑空间 图像质量,忽略视频固有时域上的特性,因而无法准确地描述客观视频质量评价和主观评价的相关性。为此,结合 视频的时域特性,提出一种改进的视频质量评价算法。该算法将视频帧图像分为边缘区域与平滑区域,分别对2 个 区域进行小波变换,并利用小波系数求得各个区域视频帧的图像质量度量值,进行加权后得出视频单帧图像质量 的度量值,对连续单帧图像进行时域融合,从而求得整个视频的质量度量值。在LIVE 视频数据库上的实验结果表 明,该算法与人类主观评价结果具有较好的一致性,斯皮尔曼相关系数达到0. 788 5。

关键词: 视频质量评价, 小波域, 特征向量, 区域划分, 时域融合, 非对称观测, 感知加权

Abstract: Most video quality algorithms regard the average of all the frames of the video image quality as the quality of the video,but these methods only take the quality of the spatial image into account,ignoring the inherent characteristics of the video on the temporal domain,therefore can not accurately describe the relevance of the objective video quality assessment and subjective video quality assessment. Thinking about temporal domain characteristics of video,this paper proposes a video quality assessment algorithm based on wavelet domain and temporal domain. The image of the video is divided into the smooth region and the edge region. The two regions are applied with wavelet transform to obtain the wavelet coefficients of each region of a video frame. The frame quality is obtained by weighting the two parts of the frame. The temporal pooling is adopted to obtain the quality of the video. Experimental results on LIVE video database show that this algorithm and human subjective evaluation results have a good consistency,and Spearman Rank Order Correlation Coefficient(SROCC) value reaches 0. 788 5.

Key words: video quality assessment, wavelet domain, feature vector, regional division, temporal domain fusion, asymmetric observation, perceptual weighting

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