Singular value decomposition(SVD)-based image quality evaluation method

Abstract

The invention discloses a singular value decomposition(SVD)-based image quality evaluation method, which comprises the following five steps of: 1, respectively dividing an original image and a distorted image with the size of m*n into m*n/k*k image blocks; 2, programming all the image blocks according to an SVD principle so as to implement SVD; 3, calculating structural distortion in left and right singular vectors U and V and U(p) and V(p) of the image blocks of the original image and the distorted image, and simultaneously calculating brightness distortion in singular value characteristic vectors S and S(p) of the image blocks of the original image and the distorted image; 4, integrating the brightness distortion and structural distortion of the image blocks of the original image and the distorted image to obtain a formula D1, performing cycle calculation to obtain D1 values of all m*n/k*k image blocks, and solving a mean value; and 5, integrating the image quality evaluation parameters, defining an SVD-based image quality evaluation index, wherein when a QSVD value is zero, the image quality is the best, the QSVD value is increased with the change of QSVD, and the image quality is worse. The SVD-based image quality evaluation method has the advantages of practical value and wide application prospect in the field of image quality evaluation.

Description

technical field The invention proposes an image quality evaluation method based on singular value decomposition, which belongs to the field of image quality evaluation. It specifically relates to an objective image quality evaluation method based on image singular value decomposition and obtaining singular feature vectors containing image structure information and brightness information. Background technique Image quality evaluation is one of the key technologies of image processing system, and many factors affect the judgment of image quality. The method and index of image quality evaluation can provide useful guidance on how to improve image quality and select corresponding image processing methods in application fields such as video coding and image processing systems. Image quality assessment methods are mainly divided into two categories: subjective quality assessment methods and objective quality assessment methods. The subjective evaluation method refers to the com...

AI Technical Summary

Problems solved by technology

For example, Qiansen et al. proposed a quality evaluation method based on singular value decomposition, which uses the properties of the angle between different singular value eigenvectors to evaluate image quality. consistency

Images