An Evaluation Method of Image Ringing Effect

Abstract

The invention belongs to the technical field of image processing and in particular to a method for evaluating an image ringing effect. The method comprises steps of: calculating the edge information on the chrominance components of a central pixel in horizontal and vertical directions, and using a maximum value as a first edge information maximum value; calculating the variance of an image block;calculating a difference maximum value between the central pixel and a filtered pixel selected from the (k-1)th row and the (k-1)th column of the image block; calculating the edge information on the brightness component of the central pixel; determining whether a frequency area where the central pixel is located is a flat area; calculating the current brightness of the central pixel if so; and acquiring first filtering strength according to the current brightness as the filtering strength of the central pixel. The method, while ensuring a de-noising effect, avoids the trailing phenomenon of the de-noised image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an evaluation method of image ringing effect. Background technique [0002] Image quality evaluation is mainly to measure and analyze various parameters of the image, and use image parameters to represent the quality of the clothing image, so as to obtain the information we need. Image quality evaluation is widely used in national defense and military fields such as missile imaging guidance and target reconnaissance, as well as civilian fields such as medical detection, security monitoring, and intelligent transportation. Especially in the application of remote sensing images, it is used for interpretation and interpretation in reconnaissance, to discover and identify targets, and to obtain military intelligence. In surveying and mapping applications, it is used for stereoscopic mapping, locating targets, and obtaining digital geographic information. The quality of rem...

AI Technical Summary

Problems solved by technology

[0003] At present, domestic image quality evaluation methods are mostly aimed at blur and noise, without considering the relationship between evaluation parameters; the evaluation parameters used are concentrated in the mathematical statistics of images; the physical imaging process is not fully considered and most of them use a single feature to represent image quality. Among them, the relatively simple image quality measurement parameters include mean square error and peak signal-to-noise ratio, but because these two image quality evaluation methods do not reflect the content information of the image itself, they cannot meet the laws of the human visual system.

Other commonly used image statistical information such as angular second moment, gray value, information entropy, and edge energy are used to objectively evaluate image quality. Although there are many evaluation methods, the evaluation methods are messy and cannot clearly describe an image. Provide effective support for equipment improvement and follow-up research

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