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"

1 USMAN M, HE X, XU M, et al. Survey of error concealment techniques: research directions and open issues[C]//Picture Coding Symposium. Cairns, Australia: IEEE, 2015: 233-238.
2 WAN S, YANG F, XIE Z. Evaluation of video quality degradation due to packet loss[C]//International Symposium on Intelligent Signal Processing and Communication Systems. Chengdu, China: IEEE, 2010: 1-4.
3 CHEN N, JIANG X, WANG C, et al. Study on relationship between network video packet loss and video quality[C]//International Congress on Image and Signal Processing. Shanghai, China: IEEE, 2011: 282-286.
4 SAPUTRA Y M, HENDRAWAN. The effect of packet loss and delay jitter on the video streaming performance using H.264/MPEG-4 Scalable Video Coding[C]//International Conference on Telecommunication Systems Services and Applications. Denpasar-Bali, Indonesia: IEEE, 2016.
5 BONDZULIC B P , PAVLOVIC B Z , PETROVIC V S , et al. Performance of peak signal-to-noise ratio quality assessment in video streaming with packet losses[J]. Electronics Letters, 2016, 52 (6): 454- 456.
doi: 10.1049/el.2015.3784
6 UHRINA M, VACULIK M. The impact of bitrate and packet loss on the video quality of H.264/AVC compression standard[C]//International Conference on Telecommunications and Signal Processing. Prague, Czech Republic: IEEE, 2015: 1-6.
7 CHEN N, JIANG X, WANG C. Impact of packet loss distribution on the perceived IPTV video quality[C]//International Congress on Image and Signal Processing. Chongqing, China: IEEE, 2013: 38-42.
8 PAULIKS R, SLAIDINS I, TRETJAKS K, et al. Assessment of IP packet loss influence on perceptual quality of streaming video[C]//Asia Pacific Conference on Multimedia and Broadcasting. Bali, Indonesia: IEEE, 2015: 1-6.
9 刘河潮, 常义林, 元辉, 等. 一种网络丢包的无参考视频质量评估方法[J]. 西安电子科技大学学报, 2012, 39 (2): 29- 34.
doi: 10.3969/j.issn.1001-2400.2012.02.006
LIU Hechao , CHANG Yilin , YUAN hui , et al. No-reference video quality assessment over the IP network based on packet loss[J]. Journal of Xidian University, 2012, 39 (2): 29- 34.
doi: 10.3969/j.issn.1001-2400.2012.02.006
10 刘河潮, 杨付正, 常义林, 等. 考虑丢包特性的无参考网络视频质量评估模型[J]. 西安交通大学学报, 2012, 46 (2): 130- 134.
LIU Hechao , YANG Fuzheng , CHANG Yilin , et al. A no-reference assessment model for quality of networked video based on features of packets loss[J]. Journal of Xi'an Jiaotong University, 2012, 46 (2): 130- 134.
11 TANG S , ALFACE P R . Impact of random and burst packet losses on H.264 scalable video coding[J]. IEEE Transactions on Multimedia, 2014, 16 (8): 2256- 2269.
doi: 10.1109/TMM.2014.2348947
12 KORHONEN J . Study of the subjective visibility of packet loss artifacts in decoded video sequences[J]. IEEE Transactions on Broadcasting, 2018, 64 (2): 354- 366.
doi: 10.1109/TBC.2018.2832465
13 GAO P , PENG Q , WEI X . Analysis of pacet-loss-induced distortion in view synthesis prediction-based 3-D video coding[J]. IEEE Transactions on Image Processing, 2017, 26 (6): 2781- 2796.
doi: 10.1109/TIP.2017.2690058
14 HEWAGE C T E R , MARTINI M G , APPUHAMI H D . A study on the impact of compression and packet losses on rendered 3D views[J]. Three-Dimensional Image Processing(3DIP) and Applications Ⅱ, 2012, 8290, 82901D-1- 82901D-9.
doi: 10.1117/12.909164
15 HARALICK R M , SHANMUGAM K , DINSTEIN I . Textural features for image classification[J]. IEEE Transactions on Systems Man & Cybernetics, 1973, smc-3 (6): 610- 621.
16 CHANG C C , LIN C J . Libsvm:a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2 (3): 1- 27.
17 MOORTHY A K , BOVIK A C . Blind image quality assessment:from natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 2011, 20 (12): 3350- 64.
doi: 10.1109/TIP.2011.2147325
18 MITTAL A , MOORTHY A K , BOVIK A C . No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21 (12): 4695- 4708.
doi: 10.1109/TIP.2012.2214050
19 LIU L , LIU B , HUANG H , et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing Image Communication, 2014, 9 (8): 856- 863.
20 OSZUST M . No-reference image quality assessment using image statistics and robust feature descriptors[J]. IEEE Signal Processing Letters, 2017, 24 (11): 1656- 1660.
doi: 10.1109/LSP.2017.2754539
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