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No-Reference Stereoscopic Video Quality Assessment Based on Spatial-Temporal Statistics

  • Jiufa ZhangEmail author
  • Lixiong Liu
  • Jiachao Gong
  • Hua Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)

Abstract

Stereoscopic video quality assessment (SVQA) has become the necessary support for 3D video processing while the research on efficient SVQA method faces enormous challenge. In this paper, we propose a novel blind SVQA method based on monocular and binocular spatial-temporal statistics. We first extract the frames and the frame difference maps from adjacent frames of both left and right view videos as the spatial and spatial-temporal representation of the video content, and then use the local binary pattern (LBP) operator to calculate spatial and temporal domains’ statistical features. Besides, we simulate binocular fusion perception by performing weighted integration of generated monocular statistics to obtain binocular scene statistics and motion statistics. Finally, all the computed features are utilized to train the stereoscopic video quality prediction model by a support vector regression (SVR). The experimental results show that our proposed method achieves better performance than state-of-the-art SVQA approaches on three public databases.

Keywords

Stereoscopic video quality assessment Spatial-temporal Structural statistics No-reference 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under grant 61672095 and grant 61425013.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiufa Zhang
    • 1
    Email author
  • Lixiong Liu
    • 1
  • Jiachao Gong
    • 1
  • Hua Huang
    • 1
  1. 1.Beijing Laboratory of Intelligent Information TechnologyBeijing Institute of TechnologyBeijingPeople’s Republic of China

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