Image quality evaluation method based on multi-scale geometric analysis

Abstract

The utility model discloses an image quality evaluation method based on multi-scale geometric analysis, mainly solving the problem that consistency is poor between image quality objective evaluation value and subjective evaluation value, comprising (1) The scale and direction sub-band decomposition is performed on reference image and evaluated image using multi-scale analysis method; (2) All decomposed sub-bands are weighted using contrast sensitivity function; (3) The vision perception threshold value of all coefficients are determined based on human eye vision perception property; (4) The proportion of coefficient which is more than the vision perception threshold value in sub-bands of every direction of reference image and evaluated image is calculated respectively; (5) The absolute value difference of the proportion of coefficient which is more than the vision perception threshold value in sub-bands of every direction of reference image and evaluated image is recorded, and then the evaluation measurement of image quality is defined based on the proportion absolute value difference. The utility model has the advantages of simple structure, small transmission data size, low calculating complexity, good consistency with subjective evaluation, and applicability to validity evaluation for image processing method.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an evaluation method for image quality, which can be used for image quality evaluation processing in the fields of image compression, image storage, image communication, image detection and the like. Background technique [0002] Images are obtained by observing the objective world in different forms and means with various observation systems, and can directly or indirectly act on the human eye to produce visual entities. About 75% of the information that humans get from the outside world is obtained from images. With the development of signal processing and computer science and technology, image engineering has also become a subject with rich content and rapid development. An image system includes image acquisition, display, storage, communication, processing and analysis. It is widely used in various fields of the national economy, such as: scientific research, indus...

AI Technical Summary

Problems solved by technology

This is because, if the RR feature data rate is larger, the more information that can contain the reference image, the more accurate the prediction will be, but this will also cause a great burden to transmit these parameters; on the contrary, the smaller the amount of data, the more Easier to teleport, but also worse final predictions

[0011] 3) No reference type

Aiming at the fact that the wavelet subband coefficients of natural images satisfy the generalized Gaussian distribution, this method approximates the distribution with the wavelet subband coefficients of the distorted image, and then estimates the quality of the distorted image according to the fitting error, and obtains good evaluation results. This method has a complex structure and high calculation cost, and this method does not optimally solve the contradiction between the evaluation effect and the cost of transmitting data volume

Since the coefficient distribution of the wavelet subband only utilizes the statistical independence of the subband coefficients, but the non-independence between the coefficients exists in all directions, scales, and positions, so the effect is not ideal.

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