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Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 pp 147–162Cite as

A Deep Blind Image Quality Assessment with Visual Importance Based Patch Score

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11362))

Abstract

Convolutional neural networks (CNNs)-based no-reference image quality assessment (NR-IQA) suffers from insufficient training data. The conventional solution is splitting the training image into patches, assigning each patch the quality score, while the assignment of patch score is not consistent with the human visual system (HVS) well. To address the problem, we propose a patch quality assignment strategy, introducing the weighting map to describe the degree of visual importance of each distorted pixel, integrating the weighting map and the feature map to pool the quality score of each patch. With the patch quality, a CNNs-based NR-IQA model is trained. Experimental results demonstrate that proposed method, named as blind image quality metric with improved patch score (BIQIPS), improves the performance on most of the distortion types, especially on the types of local distortions, and achieves state-of-the-art prediction accuracy among the NR-IQA metrics.

Supported by National Nature Science Foundation of China (No. 61572058) and National Key R&D Program of China (No. 2017YFB1002702).

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Zhengyi Lv, Xiaochuan Wang, Kai Wang & Xiaohui Liang

Authors
  1. Zhengyi Lv
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  2. Xiaochuan Wang
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  3. Kai Wang
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  4. Xiaohui Liang
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Corresponding author

Correspondence to Xiaohui Liang .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Lv, Z., Wang, X., Wang, K., Liang, X. (2019). A Deep Blind Image Quality Assessment with Visual Importance Based Patch Score. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-20890-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20889-9

  • Online ISBN: 978-3-030-20890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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