A Pre-Saliency Map Based Blind Image Quality Assessment via Convolutional Neural Networks

Zhengxue Cheng, Masaru Takeuchi, Jiro Katto

    研究成果: Conference contribution

    10 被引用数 (Scopus)

    抄録

    In recent years, various approaches have been investigated towards blind image quality assessment (IQA) with high accuracy and low complexity. In this paper we develop a pre-saliency map based blind IQA method, which takes advantage of saliency information in prior of quality prediction for performance enhancement by two steps. 1) We split the image into patches and design a convolution neural network (CNN) to predict the patch-wise quality score. Then we explore the relation between image saliency information and CNN prediction error to present a statistical analysis. 2) Based on the analysis, we propose a patch quality aggregation algorithm by removing non-salient patches which are likely to bring large prediction error and assigning large weights for salient patches. Experimental results validate that our method can achieve high accuracy (0.978) with subjective quality scores, which outperforms existing IQA methods. Meanwhile, the proposed method can reduce 52.7% computational time than the IQA without pre-saliency map.

    本文言語English
    ホスト出版物のタイトルProceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ77-82
    ページ数6
    2017-January
    ISBN(電子版)9781538629369
    DOI
    出版ステータスPublished - 2017 12 28
    イベント19th IEEE International Symposium on Multimedia, ISM 2017 - Taichung, Taiwan, Province of China
    継続期間: 2017 12 112017 12 13

    Other

    Other19th IEEE International Symposium on Multimedia, ISM 2017
    CountryTaiwan, Province of China
    CityTaichung
    Period17/12/1117/12/13

    ASJC Scopus subject areas

    • Media Technology
    • Sensory Systems

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