Objective evaluation method of no-reference stereo image quality based on binocular visual perception
A technology for objective quality evaluation and stereoscopic images, applied in stereo systems, image communication, television, etc.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2017-05-03
Smart Images

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Abstract
Description
technical field
[0001] The invention relates to a method for evaluating the quality of a stereoscopic image, in particular to an objective evaluation method for the quality of a stereoscopic image without reference based on binocular visual perception. Background technique
[0002] The rapid development of the digital information age has led to an upsurge of research in the field of images. The process of image acquisition, compression, processing, transmission, storage and display will inevitably bring different degrees and types of distortion, and these distortions will directly affect the quality of the image. Therefore, designing an effective image quality evaluation mechanism is an important part of the image / video system. Image quality objective evaluation methods can be divided into full-reference, semi-reference and no-reference types. The evaluation results of the full-reference image quality objective evaluation method are relatively accurate and feasible, but be...
Examples
Embodiment Construction
[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.
[0044] According to the human stereo vision characteristics of binocular fusion, binocular competition and depth perception, the present invention proposes a no-reference stereo image quality objective evaluation method based on binocular vision perception, which first simulates binocular stereo vision and utilizes energy The gain control model constructs the convergent cyclopene map of the distorted stereo image, and at the same time, uses the left viewpoint image and the right viewpoint image to construct the left disparity map, the right disparity map, the uncertain left map and the uncertain right image; then, the curve wave is extracted from the convergent cyclopene map Domain features, and extract generalized Gaussian fitting parameter features and lognormal distribution fitting parameter features on the left disparity map and right dispa...