Tensor space-based no-reference high dynamic range image objective quality evaluation method

Abstract

The invention discloses a tensor space-based no-reference high dynamic range image objective quality evaluation method. From image data, a colorful high dynamic range image is represented with a three-order tensor; then a distorted high dynamic range image is subjected to tensor decomposition through a Tucker decomposition algorithm in tensor decomposition to obtain three characteristic images, namely, a first characteristic image, a second characteristic image and a third characteristic image are obtained, wherein the three characteristic images fuse color information of the high dynamic range image; a manifold structure characteristic of the first characteristic image and perception detail contrast characteristics of the second and third characteristic images are extracted; and an objective quality evaluation value of the distorted high dynamic range image is calculated by utilizing a support vector regression method in machine learning, so that the objective quality evaluation of the no-reference colorful high dynamic range image is realized, the evaluation effect is remarkably improved, and the correlation between an objective evaluation result and subjective perception is effectively improved.

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. more and more researchers' attention.

[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 evalua...

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