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No-Reference Video Shakiness Quality Assessment

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10115))

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Abstract

Video shakiness is a common problem for videos captured by hand-hold devices. How to evaluate the influence of video shakiness on human perception and design an objective quality assessment model is a challenging problem. In this work, we first conduct subjective experiments and construct a data-set with human scores. Then we extract a set of motion features related to video shakiness based on frequency analysis. Feature selection is applied on the extracted features and an objective model is learned based on the data-set. The experimental results show that the proposed model predicts video shakiness consistently with human perception and it can be applied to evaluating the existing video stabilization methods.

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Acknowledgment

This work was partially supported by National Basic Research Program of China (973 Program) under contract 2015CB351803 and NSFC under contracts 61572042, 61390514, 61421062, 61210005, 61527084, as well as the grant from Microsoft Research-Asia.

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Authors and Affiliations

  1. National Engineering Laboratory for Video Technology, Cooperative Medianet Innovation Center, School of EECS, Peking University, Beijing, 100871, China

    Zhaoxiong Cui & Tingting Jiang

Authors
  1. Zhaoxiong Cui
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  2. Tingting Jiang
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Corresponding author

Correspondence to Tingting Jiang .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Cui, Z., Jiang, T. (2017). No-Reference Video Shakiness Quality Assessment. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10115. Springer, Cham. https://doi.org/10.1007/978-3-319-54193-8_25

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  • DOI: https://doi.org/10.1007/978-3-319-54193-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54192-1

  • Online ISBN: 978-3-319-54193-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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