Video quality assessment method estimated on basis of macroblock domain distortion degree

Abstract

The invention discloses a video quality assessment method estimated on the basis of a macroblock domain distortion degree. The method comprises the following steps that (1), a video image is divided into a plurality of macroblocks; (2), a blocking effect distortion degree is calculated; (3), a blurring effect distortion degree is calculated; (4), a luminance contrast degree is calculated; (5), texture complexity is calculated; (6), an exercise intensity contrast degree is calculated; (7), exercise direction consistency is calculated; (8), visual perceptibility is calculated; and (9), a video quality assessment value is calculated. During the course of video quality assessment, a model of the video quality assessment method is simple; the objective quality of a video can be obtained only when the to-be-assessed video is provided; the method is very high in flexibility; in addition, relatively accurate assessment results of various video scenes can be obtained; and the method is preferable in universality.

Description

technical field [0001] The invention belongs to the technical field of video quality evaluation, and in particular relates to a video quality evaluation method based on macroblock domain distortion degree estimation. Background technique [0002] With the rapid development of computer and network communication technology, people's demand for multimedia information is increasingly strong. In recent years, video-related applications cover various fields, such as video conferencing, video surveillance, and mobile TV. In these applications, video information needs to be compressed and transmitted before reaching the receiver, and these processes often cause video quality loss. In order to obtain a better subjective effect, it is necessary to evaluate the video quality and adjust the parameters of the encoder and transmission channel according to the results. The final receptor of the video is the human eye, and human eye observation is considered to be the most accurate method...

AI Technical Summary

Problems solved by technology

[0004] Although the existing video quality assessment methods have achieved certain results, some relatively mature models have been formed; such as the traditional video quality assessment method based on PSNR (Peak Signal-to-Noise Ratio) and Wang Zhou's titled Image quality assessment: From error visibility to structural similarity (IEEE Transactios on Image Processing, 2004, 13(4)) proposed a video quality evaluation method based on SSIM (structural similarity); but these methods did not consider the role of HVS in video quality evaluation , ignoring the impact of video content features on video quality, the accuracy needs to be improved; and it is difficult to apply to videos in different scenarios, and the universality is not high

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