Nonlinear normalization based IQA (image quality assessment) method of Laplace-Gaussian signal

Abstract

The invention discloses a nonlinear normalization based IQA (image quality assessment) method of a Laplace-Gaussian signal. The method comprises steps as follows: redundancy elimination expression is performed on images firstly and is completed through two processes including LOG filtering and nonlinear normalization; the two processes are used for eliminating first-order and second-order statistical redundancy in the images and reducing high-order statistical redundancy respectively; two computing methods are proposed to predict the subjective quality or the distortion degree of the images, and the two computing methods are marked as NLOG-MSE (mean square error) and NLOG-COR (correlation) respectively; according to NLOG-MSE, mean square errors between the original images subjected to redundancy elimination expression and test images is computed to obtain the distortion measurement of the test images, and according to NLOG-COR, correlation between the original image and test image in each point and redundancy elimination expression is computed to predict the image quality. The experimental result proves that the two computing methods have good prediction performance in the aspect of IQA, the NLOG-MSE method has simple computation, and the application of the NLOG-MSE method in other fields is greatly facilitated.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image quality full-reference evaluation method, in particular to an image quality evaluation method based on a nonlinear normalized Laplace-Gaussian signal. Background technique [0002] Image quality assessment (IQA) algorithms have a wide range of applications in the field of image processing, including quality monitoring, image restoration and reconstruction, algorithm design and optimization, resource allocation, parameter estimation, and perceptual video coding. [0003] The research on image quality evaluation methods has achieved many excellent results. According to whether a reference image is required as a standard, image quality evaluation methods can be divided into three categories. The first type is a full-reference image quality evaluation method, which is mainly used under the condition that all information of the original reference image can be obtained. At prese...

AI Technical Summary

Problems solved by technology

Compared with the first type, the amount of information is much less, so it is more difficult

Images