No-reference video quality evaluation method based on space-time domain natural scene statistics characteristics

Abstract

Objective video quality evaluation is one of the important research points for QoE service in the further. The invention provides a video quality evaluation method based on no-reference natural scene statistics (NSS). Firstly, through analyzing a video sequence, corresponding statistical values of each pixel point and the adjacent point are calculated and space domain statistics characteristics of the video are thus obtained. A predication image of an n+1 frame is obtained according to a motion vector and in combination with a reference frame n, a motion residual image is obtained, and statistical distribution after DCT transformation is carried out on the residual image is observed. Values obtained in the former two steps are used for respectively calculating a mahalanobis distance between the space domain characteristics and the natural video characteristics and a mahalanobis distance between the time domain characteristics and the natural video characteristics so as to obtain statistical differences between a distorted video and the natural video, and the quality of a single-frame image is obtained when the time domain information and the space domain information are converged. Finally, a time domain aggregation strategy on the basis of visual hysteresis effects is adopted to obtain the objective quality of the final video sequence.

Description

technical field [0001] The invention relates to a method for objectively evaluating video quality without reference, in particular to an estimation of the influence of network distortion on video quality based on a Natural Scene Statistics (NSS) algorithm. technical background [0002] With the development of wireless networks and smart terminals, more and more users start to use video services on mobile terminals, such as video conferencing and video chatting. Due to lossy video compression and network packet loss, the video quality seen by users may be degraded. In order to ensure the user experience of video services, more and more researches have begun to focus on how to measure video quality. The most reliable quality evaluation method is subjective quality evaluation, but this method is time-consuming and laborious, and it cannot be used in real-time business. The objective quality assessment method is mainly dedicated to establishing an algorithm based on the content...

AI Technical Summary

Problems solved by technology

In video quality assessment based on frame images, many algorithms evaluate the quality of specific distortion types, such as evaluating block effects caused by compression, and evaluating Gaussian blur. However, in practice, the type of video distortion is often unknown Yes, other methods are based on training. By extracting some parameters from the image, and then combining machine learning methods to obtain image quality, it is more dependent on the training library and can only achieve good results in some specific scenarios.

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