A 3D image quality assessment method based on visual saliency and depth maps

Abstract

The invention discloses a 3D image quality evaluation method based on visual saliency and depth map. Use the stereo matching algorithm to obtain the disparity map; use the spectral residual algorithm to obtain the saliency map of the image, use the structural similarity algorithm to obtain the similarity of brightness, contrast, and contrast, and use the Gaussian color model to obtain metrics such as chroma similarity features; Finally, the Log Gabor filter is used to extract multi-scale features from the reference image and the distorted image, and the edge texture features of the left and right images of multi-scale and multi-directional are obtained, and the similarity calculation is performed to obtain the index features of image quality evaluation, and the support vector machine is used to carry out Regression prediction, get the objective quality score, complete the mapping to the stereoscopic image quality, and get the final stereoscopic image quality evaluation. The objective evaluation and subjective evaluation of the full-reference image quality evaluation method proposed by the present invention have good consistency, and are superior to the traditional three-dimensional image quality evaluation method.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a 3D image quality evaluation method based on visual salience and depth maps. Background technique [0002] Vision is the main channel for human beings to obtain information from the outside world, and people obtain more than 80% of the information through vision. The most important carrier of visual information is image. Therefore, image processing technology, including acquisition and display, has become an important part of people's daily life. [0003] Three-dimensional (3D) imaging techniques, including processing stages such as 3D scene capture, 3D compression, 3D transmission, rendering and display, have attracted enormous research attention in the past decade. Although digital image processing and its related fields have made amazing progress in these years, they still face the problem that the quality of visual signals cannot fully meet the current ...

AI Technical Summary

Problems solved by technology

Although subjective evaluation based on the judgment of human observers is the most effective means of predicting image quality, it has limitations: it is time-consuming, expensive, and cannot be implemented in real-time systems. accuracy of judgment

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