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LaG-DESIQUE: A Local-and-Global Blind Image Quality Evaluator Without Training on Human Opinion Scores

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Big Data (Big Data 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 945))

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Abstract

This paper extends our previous DESIQUE [1] algorithm to a local-and-global way (LaG-DESIQUE) to blindly measure image quality without training on human opinion scores. The local DESIQUE extracts block-based log-derivative features and evaluates image quality through measuring the multivariate Gaussian distance between selected natural and test image patches. The global DESIQUE extracts image-based log-derivative features and image quality is estimated based on a two-stage framework, which was trained on a set of regenerated distorted images with their quality scores estimated by MAD [2] algorithm. The overall quality is the weighted average of local and global DESIQUE scores. Test on several image databases demonstrates that LaG-DESIQUE performs competitively well in predicting image quality.

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Acknowledgements

The work was jointly supported by the National Key Research and Development Program of China under Grant No. 2018YFC0807500, the National Natural Science Foundations of China under grant No. 61772396, 61472302, 61772392, the Fundamental Research Funds for the Central Universities under grant No. JB170306, JB170304, and Xian Key Laboratory of Big Data and Intelligent Vision under grant No. 201805053ZD4CG37.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xian, 710071, Shaanxi, China

    Ruyi Liu & Qiguang Miao

  2. Xian Key Laboratory of Big Data and Intelligent Vision, Xian, 710071, Shaanxi, China

    Ruyi Liu & Qiguang Miao

  3. School of Electronic and Information Engineering, Xian Jiaotong University, Xian, 710049, Shaanxi, China

    Yi Zhang

  4. Department of Electrical and Electronic Engineering, Shizuoka University, Hamamatsu, Shizuoka, 432-8561, Japan

    Damon M. Chandler

  5. School of Information Science and Engingeering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Tiange Liu

Authors
  1. Ruyi Liu
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  2. Yi Zhang
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  3. Damon M. Chandler
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  4. Qiguang Miao
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  5. Tiange Liu
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Corresponding author

Correspondence to Qiguang Miao .

Editor information

Editors and Affiliations

  1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China

    Zongben Xu

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Xidian University, Xi'an, Shaanxi, China

    Qiguang Miao

  4. Chinese Academy of Sciences, Beijing, China

    Yunquan Zhang

  5. Zhejiang University, Hangzhou, China

    Jiajun Bu

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Liu, R., Zhang, Y., Chandler, D.M., Miao, Q., Liu, T. (2018). LaG-DESIQUE: A Local-and-Global Blind Image Quality Evaluator Without Training on Human Opinion Scores. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_18

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  • DOI: https://doi.org/10.1007/978-981-13-2922-7_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2921-0

  • Online ISBN: 978-981-13-2922-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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