Algorithm for evaluating color image quality

Abstract

The invention discloses an algorithm for evaluating color image quality. The algorithm comprises: firstly performing HSI transformation on a reference image and images to be evaluated; representing the images with hue, intensity, and saturation; using the hue, the intensity, and the saturation as the imaginary part of a quaternion; constructing and performing singular value decomposition on quaternion matrixes of the reference image and the images to be evaluated in order to obtain singular value characteristic vectors; finally using grey relational degree to calculate relational degree between the singular value characteristic vector of the reference image and the singular value characteristic vector of each image to be evaluated, wherein the more the relational degree is, the better the quality of the image to be evaluated is. The validity of the algorithm is verified by actual examples and an evaluation result of the algorithm is better than conventional methods of MSE, PSNR, and SSIM.

Description

technical field [0001] The invention belongs to the field of image evaluation, and in particular relates to a color image quality evaluation algorithm. Background technique [0002] The human eye's adaptability to brightness means that it can only recognize dozens of gray levels at any point in a complex image, but it can recognize thousands of colors, so color images provide a richer image than gray images. information. For the quality evaluation of color images, most of the methods currently used are to extract the brightness level information of the color image through some transformation, and use the grayscale image quality evaluation method to evaluate the extracted brightness level information, so as to obtain the information for the color image. However, this method obviously ignores a large amount of color information contained in the image, so it is difficult to guarantee the accuracy of the evaluation results. [0003] The quaternion was first proposed by Hamilto...

AI Technical Summary

Problems solved by technology

For the quality evaluation of color images, most of the methods currently used are to extract the brightness level information of the color image through some transformation, and use the grayscale image quality evaluation method to evaluate the extracted brightness level information, so as to obtain the information for the color image. However, this method obviously ignores a large amount of color information contained in the image, and it is difficult to guarantee the accuracy of the evaluation results.

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