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

Screen image quality evaluation method based on sparse representation

A technology for screen image and quality evaluation, applied in the field of screen image quality evaluation based on sparse representation, can solve problems such as inability to apply screen image quality evaluation, and achieve the effects of high consistency, simple preprocessing method and less time-consuming

Inactive Publication Date: 2019-03-29
TIANJIN UNIV
View PDF5 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Many algorithms for assessing the quality of natural images are not suitable for quality assessment of screen 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
  • Screen image quality evaluation method based on sparse representation
  • Screen image quality evaluation method based on sparse representation
  • Screen image quality evaluation method based on sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The screen image quality evaluation method based on sparse representation of the present invention, assuming that the distorted image is F, comprises the following steps:

[0029] Step 1: Calculate the absolute gradient map of the distorted screen image. First grayscale the distorted screen image, and then find the absolute gradient value F(x, y) at the pixel position (x, y):

[0030] F(x,y)=|F h (x,y)|+|F v (x,y)|

[0031] in

[0032]

[0033]

[0034] In the formula, I(x, y) represents the brightness layer of the distorted screen image, Represents a linear convolution kernel, |F h (x, y) | and |F v (x, y)| represent the absolute gradient values ​​along the horizontal and vertical directions, respectively, and the Gaussian partial guide filter T γ , the calculation formula of γ∈(h, v) is:

[0035]

[0036] where σ represents the parameters of the Gaussian function g(x, y|σ).

[0037] Step 2: Calculate the relative gradient direction map and the relat...

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 relates to a screen image quality evaluation method based on sparse representation, comprising the following steps: first, calculating three kinds of gradient maps of distorted screen images, including absolute gradient maps, relative gradient patterns and relative gradient maps; Step 2: extracting the texture features of the screen image, and extracting the gradient direction histogram features from the absolute gradient image, the relative gradient direction image and the relative gradient image obtained in step 2. constructing a learning dictionary; performing sparse representation of the feature vector of the test screen image. Step 5: a pooling stage of mass fraction. The screen image quality evaluation method provided by the invention accurately predicts the quality fraction after the screen image is distorted by using the image second-order partial derivative information and the sparse representation.

Description

technical field [0001] The invention belongs to the field of digital image processing, and relates to a screen image quality evaluation method based on sparse representation. Background technique [0002] With the rapid development of the Internet and various electronic devices, people are not only exposed to natural images, but also various screen images in daily life. Natural images are often captured by digital cameras, whereas screen images are captured by computer or mobile device screenshots. During the transmission, reception and encoding of screen images, it will inevitably be interfered by various types of image distortion, which will affect human visual perception of screen images. For example, if teleconferencing and online cloud video are affected by unfavorable factors such as transmission distortion and network delay, it is necessary to evaluate the quality of online real-time screen images so that service providers can dynamically adjust source location strat...

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 Applications(China)
IPC IPC(8): G06T7/00G06K9/46G06K9/62
CPCG06T7/0002G06T2207/30168G06T2207/20081G06T2207/10004G06V10/50G06V10/462G06F18/28
Inventor 杨嘉琛刘佳成
Owner TIANJIN UNIV
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