Entropy-based semi-blind image quality evaluation method and system in complex wavelet domain

Abstract

The invention relates to the technical field of image processing and discloses a complex wavelet domain semi-blind image quality evaluation method and system based on entropies. The method comprises the steps that subblocks of an input original reference images and an image to be evaluated are divided, the image information entropies of all the subblocks are calculated, and the subblocks of which the entropies are greater than an average entropy of all the subblocks are selected; the phase equalization value of the subblocks of the original reference image and the image to be evaluated is calculated, and the visual weight factor of quality evaluation is set; complex wavelet transformation is carried out on the subblocks selected from the original reference image and the image to be evaluated; the amplitude and the phase information of the original reference image and the image to be evaluated are extracted respectively, and the quality of the semi-blind image is evaluated in combination with the visual weight factor. The area with the rich information in the image is selected through the information entropies to serve as a space for extracting the feature information, the amplitude and the phase information of the images are utilized comprehensively, the objective evaluation based on the main features of the images are achieved, and the efficiency of evaluating the image quality is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an entropy-based complex wavelet domain semi-blind image quality evaluation method and system. Background technique [0002] In the fields of image transmission, multimedia communication, and video processing, images usually suffer from a certain degree of distortion, such as image distortion caused by additive noise, data compression, geometric deformation, and motion blur. In order to facilitate the analysis and processing of subsequent images, it is usually necessary to objectively evaluate the image quality. The evaluation results will help optimize the configuration of algorithm design parameters in the image processing system, analyze distortion factors, or image restoration, etc., thereby ensuring high-quality images. quality image signal. [0003] Generally speaking, the objective evaluation of image quality includes full reference image quality evaluation metho...

AI Technical Summary

Problems solved by technology

Both of these two methods have the advantages of simple and efficient calculation, but at the same time, both of these two methods only consider the structural characteristics of the image space, ignoring the geometric features, phase features and other information of the image, which cannot reflect the human eye's perception of the image well. It is also difficult to better reflect the subjective and objective consistency of image quality evaluation

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