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A no-reference quality assessment for contrast-distorted image based on improved learning method

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Abstract

No-reference image quality assessment (NR-IQA), which aims to predict image quality without accessing to reference images, is a fundamental and challenging problem in the field of image processing. Nevertheless, there are few researches about contrast-distorted images and results of the existing NR-IQA methods which cannot be in accordance with the subjective assessment results further. Therefore, an effective NR-IQA method for contrast-distorted image is proposed in this paper. Firstly, the proposed method extracts five features of all images from the database based on the natural scene statistics (NSS) model. Then the curve fitting method is subsequently represented to calculate values of natural image features. Finally, an improved Support Vector Regression (SVR) learning method based on grid search is proposed to establish the mapping between image feature values and the quality score of a test image. Experiments proved that the proposed method is effective when compared with other related state-of-the-art image quality assessment (IQA) methods based on three standard databases.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Anhui University, Hefei, 230601, China

    Yaojun Wu, Yonghe Zhu & Yang Yang

  2. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Weiming Zhang & Nenghai Yu

Authors
  1. Yaojun Wu
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  2. Yonghe Zhu
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  3. Yang Yang
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  4. Weiming Zhang
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  5. Nenghai Yu
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Correspondence to Yang Yang.

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This work was supported in part by the Natural Science Foundation of China under Grants 61502007, 61572452, in part by the Natural Science Research Project of Anhui province under Grant 1608085MF125, in part by the NO.58 China Post-doctoral Science Foundation under Grant 2015M582015, in part by the Backbone Teacher Training Programof Anhui University, in part by the Doctoral Scientific Re-search Foundation of Anhui University under Grant J01001319.

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Wu, Y., Zhu, Y., Yang, Y. et al. A no-reference quality assessment for contrast-distorted image based on improved learning method. Multimed Tools Appl 78, 10057–10076 (2019). https://doi.org/10.1007/s11042-018-6524-1

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