Stereoscopic video objective quality evaluation method based on machine learning

Abstract

The invention discloses a stereoscopic video objective quality evaluation method based on machine learning. When spatial domain quality of luminance component images of single-frame images is evaluated, each image block of luminance component images of each frame of image in original and distorted stereoscopic videos is subjected to singular value decomposition, and dot product of singular vectors obtained from singular value decomposition is adopted to evaluate distortion degree of each frame of image in the distorted stereoscopic video. Because the singular vectors can greatly reflect structural information of images, when the dot product of the singular vectors is adopted to evaluate the quality of the images, changes of the structural information are considered, and therefore, evaluation results can reflect changes of visual quality of the stereoscopic video more objectively when the stereoscopic video is under various kinds of distortion influences. According to the stereoscopic video objective quality evaluation method based on machine learning, a method of machine learning is adopted to process the relations between objective quality evaluation predicted values and the quality of a left-view point video and a right-view point video, and degree of difference among point views of the left-view point video and the right-view point video, and therefore, evaluation results which are more consistent with human visual perception can be effectively obtained.

Description

technical field [0001] The invention relates to a video quality evaluation technology, in particular to a machine learning-based objective quality evaluation method for stereoscopic video. Background technique [0002] With the rapid development of video coding technology and 3D display technology, 3D video has successfully attracted the attention of the public; stereoscopic video composed of two viewpoints is the simplest 3D video format, and the research on 3D video began with stereoscopic video. Stereoscopic video will inevitably be distorted after a series of links such as collection and processing. Stereoscopic video quality evaluation is a technology for evaluating the quality of distorted video, which is of great significance to the development of stereoscopic video technology. [0003] Stereoscopic video quality evaluation methods can be divided into two types: subjective evaluation and objective evaluation. Although the subjective evaluation method can obtain more ...

AI Technical Summary

Problems solved by technology

The consistency between the objective results and subjective perception obtained by this simple stereoscopic video objective quality assessment method extended from the 2D image / video quality assessment method is not ideal.

Images