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Error sensitivity model based on spatial and temporal features

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Abstract

Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61301112, 61828105 and 61601278, Chen Guang Project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant No.17CG41.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, 200444, China

    Ran Ma, Tong Li, Dezhi Bo & Ping An

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, 200444, China

    Ran Ma & Ping An

  3. Global Big Data Technologies Centre, University of Technology Sydney, NSW, Sydney, 2007, Australia

    Qiang Wu

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  1. Ran Ma
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  2. Tong Li
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  3. Dezhi Bo
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Correspondence to Ran Ma.

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Ma, R., Li, T., Bo, D. et al. Error sensitivity model based on spatial and temporal features. Multimed Tools Appl 79, 31913–31930 (2020). https://doi.org/10.1007/s11042-020-09407-2

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