Volume 30 Issue 2
Apr.  2021
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FAN Kefeng, LIANG Jiyun, LI Fei, QIU Puye. CNN Based No-Reference HDR Image Quality Assessment[J]. Chinese Journal of Electronics, 2021, 30(2): 282-288. doi: 10.1049/cje.2021.01.008
Citation: FAN Kefeng, LIANG Jiyun, LI Fei, QIU Puye. CNN Based No-Reference HDR Image Quality Assessment[J]. Chinese Journal of Electronics, 2021, 30(2): 282-288. doi: 10.1049/cje.2021.01.008

CNN Based No-Reference HDR Image Quality Assessment

doi: 10.1049/cje.2021.01.008

The National Key Research and Development Program of China 2019YFB1405503

2019 Public Service Platform of Industrial Technology Foundation of MIIT 2019-00895-2-1

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  • Author Bio:

    LIANG Jiyun   was born in November 1994, Master of engineering. His research interests include video coding and image processing. (E-mail: 18589837505@163.com)

    LI Fei   was born in July 1985, senior engineer.Her research interests include cloud computing and database, etc. (E-mail: fiona2022@163.com)

    QIU Puye    was born in August 1994, Master of Science.His research interests include video coding and technical system management. (E-mail: qiupy@cesi.cn)

  • Corresponding author: FAN Kefeng   (corresponding author) was born in December 1978, Ph.D., post-doctorate, researcher-level senior engineer. His research interests include intelligent information processing and cyber security. (E-mail: fankf@126.com)
  • Received Date: 2020-05-06
  • Accepted Date: 2020-06-09
  • Publish Date: 2021-03-01
  • Motivated by the problems of nonuniversality and over-reliance on the original reference image in High dynamic range (HDR) Image quality assessment (IQA), a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed. The Salience detection by self-resemblance (SDSR) algorithm which extracts the salient regions of the HDR image, is used to simulate the human visual attention mechanism. Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity. And this network consists of an Error estimation network (Error-net), a Perceptual resistance network (PR-net) and a mixing function. The experimental results indicate that the method proposed has high consistency with subjective perception, and the value of assessment metrics Spearman rank-order correlation coefficient (SROCC), Pearson product-moment correlation coefficient (PLCC) and Root mean square error (RMSE) correspondingly reaches 0.941, 0.910 and 8.176 as well. It is comparable with classic full-reference HDR IQA methods.
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