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

A technology with high dynamic range and objective quality, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as inconsistency in human visual perception and ignorance of color information

Active Publication Date: 2017-12-12
NINGBO UNIV
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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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  • Tensor space-based no-reference high dynamic range image objective quality evaluation method

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Embodiment Construction

[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0026] A non-reference high dynamic range image objective quality evaluation method based on tensor space proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:

[0027] ① Denote the distorted high dynamic range image to be evaluated as S dis ; then put S dis Represented in the form of a third-order tensor, denoted as V dis ; Then use the existing Tucker3 decomposition algorithm to V dis Perform tensor decomposition to obtain V dis The kernel tensor of , denoted as ξ dis ; Then put ξ dis The 1st channel of the S dis The first eigenimage of ξ will be dis The 2nd channel of the S dis The second eigenimage of ξ will be dis The 3rd channel of the S dis The third eigenimage of ; where, S dis has width W and height H, S dis for color images.

[0028] ②S...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T7/11G06K9/46
CPCG06T5/006G06T5/007G06T7/11G06T2207/20081G06V10/56
Inventor 蒋刚毅管非凡郁梅彭宗举陈芬
Owner NINGBO UNIV
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