Non-reference image quality assessment method based on information entropy characters

Abstract

The invention relates to an image quality assessment method, in particular to a non-reference image quality assessment method based on information entropy characters, and belongs to the field of image analyzing. The method comprises the first step of carrying out Contourlet conversion on a distorted image to obtain N*M conversion sub-bands, the second step of dividing each conversion sub-band and the unconverted original distorted image, the third step of calculating null domain information entropy and frequency domain information entropy on each block coefficient matrix, and the fourth step of screening the blocking characters and calculating a mean value to obtain the quality character value of each conversion sub-band. The method of a support vector machine and the method of non-reference image quality assessment are utilized for testing on a test set, and quality prediction and assessment are carried out through quality character vectors corresponding to a disaggregated model, an evaluation model and the test set all of which are obtained through training. The non-reference image quality assessment method has the advantages of being high in subjective consistency, small in time complexity and good in university, can be embedded into application systems related to image quality, and has very high application value.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a no-reference image quality evaluation method based on information entropy features, which belongs to the field of image analysis. Background technique [0002] Image information has significant advantages that other forms of information cannot match. People can intuitively, accurately and efficiently use image information to perceive and understand the objective world, and process it reasonably and effectively. In the process of image acquisition, processing, transmission and recording, due to various hardware and software limitations, image distortion will inevitably occur, which will bring many negative effects on people's accurate perception of image information. For example, in the image recognition system, the distortion generated during the image acquisition process will directly affect the recognition accuracy; the teleconferencing system is easily affected by the ne...

AI Technical Summary

Problems solved by technology

[0025] The purpose of the present invention is to provide a no-reference natural image quality evaluation method based on information entropy features in order to solve the problems of low performance, poor subjective consistency, and large time complexity and space complexity of the no-reference image quality evaluation technology

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