协同超像素和视觉显著性的图像质量评价

doi:10.12052/gdutxb.210036

Abstract

Abstract: In order to achieve higher consistency between objective and subjective evaluation of image quality, a full-reference image quality evaluation method based on a synergetic strategy between superpixel and visual saliency is proposed. This method obtains the image quality score by pooling four similarities of the image features. The four similarities are: the superpixel-based local luminance similarity, the superpixel-based local chrominance similarity, the visual saliency similarity, and the Scharr gradient similarity. In particular, a parameter correction model is proposed to adaptively adjust the parameters of each similarity, to address the problem of determining the parameters of each similarity empirically. The visual saliency is introduced to design a weighting function to fuse the four similarities to obtain the global quality score. A large number of comparative experiments show that the proposed method outperforms the baseline full-reference image quality assessment methods, and has a higher correlation with the subjective evaluation.

Key words: full reference image quality assessment, superpixel, visual saliency, gradient, adaptive parameter model

CLC Number:

TP391.41

Deng Jie-hang, Yuan Zhong-ming, Lin Hao-run, Gu Guo-sheng. Superpixel and Visual Saliency Synergetic Image Quality Assessment[J].Journal of Guangdong University of Technology, 2021, 38(05): 33-39.

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