深度视觉特征与策略互补融合的图像质量评价<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201708002

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Abstract In methods of visual multiple feature pooling, visual complementary between different image features and different assessment algorithms is not taken into account. A method for complementary pooling of deeply visual feature(CPDVF) is proposed based on the integration of physiological perception of front human visual system(HVS) and psychological processing of back HVS in this paper. Firstly, two kinds of complementary features, the histogram statistics and gradient structure for visual multi-channel, are extracted and deeply processed based on visual characteristics. Secondly, the local distortion algorithm for visual histogram pooled contrast(VHPC) assessment and the local complementary similarity algorithm for visual gradient pooled contrast(VGPC) assessment are proposed. Finally, the distorted image quality is obtained with pooling of VHPC and VGPC based on psychological characteristics and regression function. The experimental results show that the CPDVF is superior to feature similarity and visual saliency in accuracy, stability and monotonicity.

Key words： Image Quality Assessment Human Visual System Statistical Feature Structural Feature Assessment Pooling

Received: 13 March 2017

ZTFLH:

TH 741.1

About author:: (LIN Zhijie, born in 1980, Ph.D., lectu-rer. His research interests include digital image processing and computer vision.)
(FENG Mingkun(Corresponding author),born in 1978, Ph.D., lecturer. His research interests include digital image processing and computer vision.)

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Cite this article:

LIN Zhijie,FENG Mingkun. Image Quality Assessment Based on Complementary Pooling of Deeply Visual Feature and Strategy[J]. , 2017, 30(8): 682-691.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201708002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I8/682

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