Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

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

A high dynamic range, tensor space technology, used in image enhancement, image analysis, image data processing, etc.

Active Publication Date: 2019-08-20
NINGBO UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • No-reference high dynamic range image objective quality assessment method based on tensor space
  • No-reference high dynamic range image objective quality assessment method based on tensor space
  • No-reference high dynamic range image objective quality assessment method based on tensor space

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com