Image quality evaluation method based on information entropy

Abstract

The invention discloses an image quality evaluation method based on information entropy. The method is used for solving the problem that an existing image quality evaluation method cannot effectivelyevaluate images of multiple distortion and deblurring distortion types. According to the method, an image library containing deblurring distortion is constructed, and then on the basis of the image library, a support vector machine is used for carrying out classified training according to extracted two-dimensional space entropy and spectrum entropy characteristics of images of the image library toobtain a distortion probability vector model; and then, score fitting is carried out on the characteristics of each type of images and the full reference quality evaluation index VSI of each type ofimages by utilizing support vector regression to obtain a specific distortion quality vector model. By means of the two models, a quality prediction score can be obtained for any image according to the information entropy characteristics of the image, and finally a final evaluation score is obtained by combining distortion detection and evaluation of ringing, residual blurring, noise and the likeof the deblurred image. Compared with the prior art, the method has the advantages of being more objective in evaluation and good in universality. In an image restoration experiment, rapid optimization of a plurality of parameters of the deblurring algorithm is realized by using the method, and the quality of the obtained restored image is obviously improved. The method can be embedded into a DaVinci system or other application systems related to image quality, and has very high practical value.

Description

technical field [0001] This patent relates to a no-reference evaluation method for image quality. The evaluation algorithm realizes blind evaluation of image quality containing multiple distortions, including common distortion and deblurring distortion. It belongs to the field of image analysis. Background technique [0002] In the field of image evaluation, according to the evaluation subject, image quality evaluation methods can be divided into two categories, one is subjective quality evaluation, and the other is objective quality evaluation. The subjective quality evaluation method first needs to formulate evaluation standards. Generally, the internationally accepted subjective evaluation standards for image quality are adopted, and then a large number of observers directly give quality scores according to the evaluation standards. This method is more intuitive, but the cost required for the research process is relatively large. And the application is limited. Therefor...

AI Technical Summary

Problems solved by technology

However, these methods can only

Images