Three-dimensional picture visual comfort evaluation method

Abstract

The invention discloses a three-dimension picture visual comfort evaluation method. The method comprises the steps that in the training stage, a plurality of three-dimensional picture pairs are selected to constitute a preference three-dimension picture pair training set, and a support vector regression training model between differential character vectors and preference values is established; in the test stage, the differential character vectors of the tested three-dimension pictures and all the pairs of training three-dimension pictures are calculated, predicted prefelirence values corresponding to all the differential character vectors are obtained by predicting according to the support vector regression training model which is obtained by training, and finally the objective visual comfort evaporation predicted value of the tested three-dimensional pictures is obtained. The three-dimension picture visual comfort evaluation method has the advantages that in the training stage, the subjective evaluation value of the training three-dimensional pictures does not need to be known, and the obtained objective visual comfort evaluation predicted value and the subjective evaluation value are kept to be highly consistent.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a stereoscopic image visual comfort evaluation method. Background technique [0002] With the rapid development of stereoscopic video display technology and high-quality stereoscopic video content acquisition technology, the quality of experience (QoE, quality of experience) of stereoscopic video is an important issue in the design of stereoscopic video systems, and visual comfort (VC, visual comfort) is an important factor affecting the visual experience quality of stereoscopic video. At present, research on the quality evaluation of stereoscopic video / image mainly considers the influence of content distortion on image quality, but seldom considers the influence of factors such as visual comfort. Therefore, in order to improve the visual experience quality of viewers, it is very important to study the objective evaluation model of visual comfort of stereoscopic video / image ...

AI Technical Summary

Problems solved by technology

[0003] The traditional stereoscopic image visual comfort evaluation method is mainly based on the machine learning method, which establishes a regression model between the stereoscopic image visual comfort features and the subjective evaluation value. Since the acquisition of the subjective evaluation value requires complex subjective experiments, it is very difficult. Time-consuming; and because the subjective evaluation value is statistically obtained through the judgment of the viewer, human factors will have a great impact on the subjective evaluation value, which will cause the established regression model to be unable to accurately predict the objective evaluation value

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