No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

IEEE Access

Liquan Shen¹  Jialu Yang¹  Ping An¹  Yongfang Wang¹ 

[1] Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China;

关键词: Stereo image quality assessment (SIQA);    no-reference;    color image;    gradient;    phase;   

DOI  :  10.1109/ACCESS.2019.2902659

来源: DOAJ

【 摘 要 】

Since a large proportion of the information that is received daily is in the form of images, a highly effective no-reference stereo image quality assessment (SIQA) method is desired. This paper proposes an improved method that covers wide quality-aware features, including the structure, color, luminance, phase, and human visual system (HVS). To be specific, since human eyes are highly sensitive to the structure of images, the gradient magnitude (GM) and gradient orientation (GO) are extracted from the left and right views of the stereo image. Considering the influence of color distortions, the images are decomposed into the RGB channels to be processed, and the local gradient of the color image is obtained by adding up the RGB gradient vectors. In addition, according to the study of the two main visual channels, especially the cyclopean and disparity maps, the binocular-related images of position and phase congruency are generated. Correspondingly, two special dictionaries for the gradient and phase are trained to parse the high-dimensional sample sets. The experimental results show that the proposed metric always achieves high consistency with human subjective assessments for both symmetric and asymmetric distortions.

【 授权许可】

Unknown