Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A No-Reference Color Image Quality Assessment Method Based on Sparse Representation

A color image, sparse representation technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems that the image quality cannot be fully reflected, and the accuracy of image quality evaluation results needs to be improved, and achieves good real-time performance and algorithm efficiency. high effect

Active Publication Date: 2017-02-15
NANJING UNIV OF INFORMATION SCI & TECH
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the characteristics of the NSS in the wavelet domain used by this method cannot fully reflect the image quality, so the accuracy of the image quality evaluation results obtained by this method still needs to be improved

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
  • A No-Reference Color Image Quality Assessment Method Based on Sparse Representation
  • A No-Reference Color Image Quality Assessment Method Based on Sparse Representation
  • A No-Reference Color Image Quality Assessment Method Based on Sparse Representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

[0029] Aiming at the deficiencies of the prior art, the present invention proposes a method for establishing an atomic dictionary suitable for no-reference quality evaluation of color images, and a corresponding sparse representation and quality score calculation method. The present invention selects atoms in the dictionary according to the maximum value of the pixel spatial correlation standard deviation of the image block in the training image in the HSV color space, and each atom is composed of the image block with the maximum pixel spatial correlation standard deviation, DMOS value, The maximum standard deviation is composed of three elements, and then the sparse representation of the image to be evaluated is obtained in the dictionary by using the matching pursuit method, and the color image quality evaluation index is constructed from the DMOS and ...

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 a no-reference color image quality assessment method on the basis of the sparse representation and belongs to the technical field of digital image processing. For images to be evaluated, the no-reference color image quality assessment method includes selecting an image block with the maximum pixel spatial correlation difference from an HSV (hue, saturation and value) color space, and matching elements in a dictionary by the image blocks, wherein each element in the dictionary is composed of the image block with the maximum pixel spatial correlation difference in a training image set, DMOS (differential mean opinion score) and the maximum difference; adopting a matching and tracking method to obtain sparse representation of the images to be evaluated in the dictionary, and setting up the color image quality assessment indexes by the DMOS and the maximum difference of the selected elements from the dictionary. The element dictionary forming method is simple and clear, the extracted characteristics more conform to visual perception of human eyes, the integral algorithm is simpler, and the quality assessment results are more accurate.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a sparse representation-based non-reference color image quality evaluation method, which belongs to the technical field of digital image processing. Background technique [0002] Image quality evaluation technology has always been a key technology in the field of image processing, which can be used to evaluate the effect of image processing methods, or to select appropriate image processing methods based on image quality. Most of the image quality evaluation technologies measure the image quality based on the difference between the processed image and the reference image; however, in most image quality evaluation application fields, it is difficult to obtain a standard image as a reference, and no reference image quality evaluation technology is needed at this time. In order to choose the appropriate image processing method according to the image quality. [0003] In recent ...

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): G06T7/00G06K9/66
CPCG06T7/0002G06T2207/30168
Inventor 张闯陈苏婷常建华
Owner NANJING UNIV OF INFORMATION SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap 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