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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 116-121.doi: 10.6040/j.issn.1672-3961.0.2018.243

• 机器学习与数据挖掘 • 上一篇    下一篇

基于视频统计特征的差错敏感度模型

李童1,2(),马然1,2,*(),郑鸿鹤1,2,安平1,2,胡翔宇1,2   

  1. 1. 上海先进通信与数据科学研究院, 上海 200444
    2. 上海大学通信与信息工程学院, 上海 200444
  • 收稿日期:2018-05-25 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 马然 E-mail:lynne_li@shu.edu.cn;maran@shu.edu.cn
  • 作者简介:李童(1995—)，女，江西赣州人，硕士研究生，主要研究方向为图像与视频信号处理. E-mail: lynne_li@shu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61301112)

An error sensitivity model based on video statistical features

Tong LI1,2(),Ran MA1,2,*(),Honghe ZHENG1,2,Ping AN1,2,Xiangyu HU1,2   

  1. 1. Shanghai Institute for Advanced Communication and Data Science, Shanghai 200444, Shanghai, China
    2. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, Shanghai, China
  • Received:2018-05-25 Online:2019-04-20 Published:2019-04-19
  • Contact: Ran MA E-mail:lynne_li@shu.edu.cn;maran@shu.edu.cn
  • Supported by:
    国家自然科学基金项目(61301112)

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摘要：

针对传统的丢包对视频质量影响研究的局限性,提出一种差错敏感度模型。对每个受损块提取周围块的丢失情况、纹理复杂度、运动矢量和梯度等可用的统计特征;对丢包视频进行差错隐藏,计算出差错敏感度;利用机器学习技术,建立统计特征和差错敏感度的关系模型。试验结果表明,相比于现有评价方法,该模型可以比较准确地预测视频帧局部差异性对不同丢包情况的敏感程度,尤其对于运动缓慢的视频序列,预测精度明显优于其他方法。

关键词: 丢包, 视频质量, 统计特征, 差错敏感度, 机器学习

Abstract:

The traditional packet losses affected the video quality, an error sensitivity model was proposed. For every damaged block, the available statistical features around the block were extracted, which included the losing status of neighboring blocks, texture complexity, motion vector and gradient. After concealing the damaged videos with error concealment methods, error sensitivities were computed. The relationship model between statistical features and error sensitivities was finally established by machine learning technology. Experimental results demonstrated that the proposed model could accurately predict the sensitivities of video frames′ local differences to different packet loss cases, compared with the state-of-art assessment methods, especially for the slow-motion video sequences, the prediction accuracy could be obviously superior to other methods.

Key words: packet loss, video quality, statistical features, error sensitivity, machine learning

中图分类号: 

  • TP391

图1

差错敏感度示意图"

图2

差错敏感度算法流程框图"

图3

受损块与邻块关系图"

图4

运动矢量关系图"

表1

序列信息"

序列 分辨率 帧率/(帧·s-1)
Shark 1920×1088 25
PoznanStreet 1920×1088 25
GT_Fly 1920×1088 25
Newspaper 1024×768 30
BookArrival 1024×768 16.67
BQmall 832×480 60

表2

不同模型在6个序列上的性能比较"

算法 Shark PoznanStreet GT_Fly Newspaper BookArrival BQmall
PLCC SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC SRCC
DIVINE 0.897 7 0.880 5 0.607 6 0.428 0 0.856 3 0.831 7 0.509 0 0.503 9 0.614 3 0.505 3 0.734 9 0.743 9
BRISQUE 0.895 0 0.881 7 0.561 8 0.427 7 0.849 3 0.806 9 0.569 3 0.514 3 0.606 3 0.508 0 0.709 6 0.726 4
SSEQ 0.891 1 0.869 3 0.409 3 0.344 2 0.815 9 0.781 0 0.445 0 0.411 5 0.517 1 0.470 8 0.636 2 0.634 7
NOREQI 0.899 0 0.877 0 0.578 7 0.434 9 0.840 8 0.820 3 0.584 8 0.490 7 0.598 9 0.565 6 0.687 6 0.704 8
Proposed 0.898 9 0.879 6 0.772 8 0.574 5 0.820 2 0.791 3 0.763 1 0.660 2 0.634 2 0.585 7 0.736 5 0.769 2

表3

跨序列测试结果"

算法 Shark BookArrival BQmall
PLCC SRCC PLCC SRCC PLCC SRCC
DIVINE 0.761 1 0.785 8 0.114 7 0.211 5 0.417 8 0.405 1
BRISQUE 0.773 5 0.717 3 0.231 8 0.233 0 0.089 8 -0.043 1
SSEQ 0.397 2 0.404 9 0.166 4 0.132 5 0.217 7 0.256 0
NOREQI 0.622 1 0.476 7 0.143 6 0.203 1 0.144 4 0.161 0
Proposed 0.814 4 0.799 1 0.554 3 0.484 2 0.560 0 0.574 7

图5

不同特征组合预测结果"

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