An objective evaluation method of stereo video quality without reference based on sparse representation

Abstract

The invention discloses a method for objectively evaluating the quality of stereoscopic video without reference based on sparse representation: the stereoscopic video is downsampled to obtain stereoscopic video pair. The left and right viewpoints of stereoscopic video pair are respectively used to obtain the monocular energy magnitude map, and the weighting factors of the left and right views areobtained. Stereoscopic video weighted the two viewpoints to get the visual perception map. Visual perception saliency map is obtained by calculating ROI on visual perception map; dictionary learning.The sparse representation of visual perception saliency graph is used to obtain the coefficient matrix and its entropy. The coefficient matrix is obtained by means of mean, variance and two-norm operation. Choosing the coefficient matrix of video pair and MOS in the video database for training, the training model is obtained. The training model is used to predict the quality of any stereo video, and the final objective prediction value is obtained. The invention makes more comprehensive and accurate objective evaluation of stereoscopic video quality according to the visual perception image andthe sparse representation as a tool.

Description

Technical field [0001] The present invention relates to the field of video processing, and more specifically, to a method for objectively evaluating the quality of a non-reference stereo video based on sparse representation. Background technique [0002] In the process of human perception of the world, visual information plays a very important role. In recent years, with the rapid development of stereoscopic shooting technology and stereoscopic display technology, more and more people have begun to watch 3D movies. Stereoscopic video can bring audiences more realistic and vivid stereoscopic scenes, and therefore has broad development prospects. However, in the process of collecting, compressing, transmitting and displaying stereoscopic video, many factors will cause the video quality to decrease. Therefore, it is very necessary to propose an effective stereoscopic video quality evaluation algorithm, which is of great significance for improving the performance of the stereoscopic...

AI Technical Summary

Problems solved by technology

Moreover, the acquisition of most videos is random, and most of the undistorted original stereoscopic videos cannot be obtained during the quality evaluation process.

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