Depth significance-based stereopicture just noticeable difference (JND) model building method

Abstract

The invention discloses a depth significance-based stereopicture just noticeable difference (JND) model building method. The method comprises the steps: calculating the horizontal parallax of a stereopicture pair to obtain the horizontal parallax map of the stereopicture pair; calculating the depth value of the stereopicture pair to obtain a depth map of the stereopicture pair; calculating the depth significance of the stereopicture pair to obtain the depth significance map SD of the stereopicture pair; and building a depth significance-based stereopicture JND model. The method fully considers the depth significance influence factors in stereopicture noticing, the model obtained by adopting the method can more accurately reflect the feeling of eyes, the stereopicture processed by the guidance of the model can be added with more noise under the condition of keeping the subjective quality basically unchanged, thus being capable of removing vision redundancy in the stereopicture videos.

Description

technical field [0001] The invention belongs to the field of perception of coding quality of stereoscopic video, and in particular relates to a method for constructing a just-perceptible difference model of stereoscopic images based on depth saliency. Background technique [0002] In recent years, while stereoscopic TV and movies have successfully brought people a good sense of immersion and visual experience, they have also brought many technical challenges, such as the transmission and storage problems brought about by the sharp increase in massive multi-viewpoint video data. In response to these problems, people have proposed stereoscopic video data compression algorithms with superior performance and formulated related stereoscopic video coding standards. However, most of these algorithms are based on the statistical characteristics of stereoscopic image pairs, and the improvement of compression efficiency mainly depends on the substantial increase in computational comple...

AI Technical Summary

Problems solved by technology

[0005] A small number of scholars have proposed a stereoscopic image perceptible difference model, but they simply add a certain stereoscopic image perception factor on the basis of the traditional two-dimensional image perceptible difference model, and do not fully consider the influence of the stereoscopic image perceptible difference. The physiological and psychological characteristics of the threshold make the model unable to accurately consider the human visual experience

[0006] In 2010, De Silva, University of Surrey, UK [1] Depth Just Perceived Threshold Model for

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