Image quality evaluating method based on visual character and structural similarity (SSIM)

Abstract

The invention discloses an image quality evaluating method based on visual character and structure similarity and relates to image quality evaluating. The method comprises the steps of: reading the pixel information of a reference image and an image to be evaluated, storing the read information in a matrix form and setting into matrixes x and y; dividing the spaces of the images into blocks unevenly and recording each block (n) so that the layouts of the image to be evaluated and the reference image are same; carrying out structural similarity (SSIM) index arithmetic on each block (n) of the reference image and the image to be evaluated; extracting the weight factor w(n) of each block (n); comprehensively weighting an SSIM index corresponding to each block (n) to obtain comprehensive image quality WMSSIM so as to finish the image quality evaluating method based on visual character and structure similarity.

Description

technical field The invention relates to an image quality evaluation, in particular to an objective image quality evaluation method based on an improved structural similarity algorithm. Background technique With the increase of mobile communication services, wireless communication has been widely used. The characteristics of the wireless communication environment (wireless channels, mobile terminals, etc.) and mobile multimedia application services are more and more support and optimization for image services and video services. With the diversification of wireless networks and the diversification and complexity of the wireless environment, the image signal will be lost during the transmission process, so the visual quality of the image is an important indicator to measure the system. At present, image quality evaluation methods can be divided into two categories: subjective evaluation and objective testing. The traditional subjective evaluation based on human visual obser...

AI Technical Summary

Problems solved by technology

In practical applications, the reference image and the image to be tested may have a relatively large difference in the brightness of each pixel in one square, while the pixels in the other square are more evenly distributed around the average brightness, and the average brightness calculation results of the two may be similar

Therefore, it is not reasonable to simply replace all pixels in the square with the average brightness for brightness distortion calculation

(2) The sensitivity of the human eye to the distortion of each block is different

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