Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 116-121.doi: 10.6040/j.issn.1672-3961.0.2018.243

• Machine Learning & Data Mining • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP391

Fig.1

Diagram for error sensitivity"

Fig.2

Flow chart of error sensitivity algorithm"

Fig.3

Relationship between damaged block andits adjacent blocks"

Fig.4

Relationship between motion vectors"

Table 1

Sequence information"

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

Table 2

The performance comparison with different models in the six video sequences"

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

Table 3

The results of cross sequences test"

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

Fig.5

Predicted results with different combinations of feature types"

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