No-reference high dynamic range image objective quality assessment method based on tensor space

Abstract

The invention discloses an objective quality evaluation method for high dynamic range images without reference based on tensor space, which starts from the image data itself, uses a third-order tensor to represent a color high dynamic range image, and then uses Tucker in tensor decomposition The decomposition algorithm performs tensor decomposition on the distorted high dynamic range image to obtain three feature images, namely the first feature image, the second feature image and the third feature image, and the colors of the high dynamic range image are integrated in the three feature images Information, then extract the manifold structure features of the first feature image and the perceptual detail contrast features of the second and third feature images, and then use the support vector regression method in machine learning to calculate the objective quality evaluation value of the distorted high dynamic range image, so that The objective evaluation of the quality of the color high dynamic range image without reference is realized, and the evaluation effect is obviously improved, thereby effectively improving the correlation between the objective evaluation result and the subjective perception.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an objective quality evaluation method for a high dynamic range image without reference based on tensor space. Background technique [0002] In recent years, with the rapid development of optical imaging technology and digital processing technology, high dynamic range (High Dynamic Range, HDR) images have attracted more and more people because of their larger brightness range, rich detail information and better visual experience. attention of more and more researchers. [0003] Similar to low dynamic range (Low Dynamic Range, LDR) images, high dynamic range images also have varying degrees of distortion during acquisition, compression, storage and transmission. These distortions will affect the visual effect of high dynamic range images, so how to construct Effective high dynamic range image quality assessment methods are of great value. [0004] The subjective quality eval...

AI Technical Summary

Problems solved by technology

[0007] An excellent objective quality evaluation method for high dynamic range images should be able to reflect the characteristics of human visual perception well, while traditional image quality evaluation methods are mostly carried out in the gray domain, ignoring color information, which is inconsistent with human visual perception, especially For vibrant high dynamic range images

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