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

Method for evaluating stereo image vision comfort level based on machine learning

A stereoscopic image and machine learning technology, applied in stereoscopic systems, image communication, television, etc., can solve problems such as inaccurate prediction

Active Publication Date: 2013-10-09
湖州优研知识产权服务有限公司
View PDF4 Cites 34 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, according to the attention characteristics of human stereo vision, the human eye is only sensitive to the visual comfort / discomfort of some visually important areas. If the global disparity statistical features are used to predict the visual comfort of visually important areas, it will lead to inaccurate Prediction gets an objective evaluation value

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
  • Method for evaluating stereo image vision comfort level based on machine learning
  • Method for evaluating stereo image vision comfort level based on machine learning
  • Method for evaluating stereo image vision comfort level based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0073] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0074] A method for evaluating visual comfort of stereoscopic images based on machine learning proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:

[0075] ① Denote the left viewpoint image of the stereoscopic image to be evaluated as {I L (x, y)}, denote the right viewpoint image of the stereo image to be evaluated as {I R (x,y)}, denote the right disparity image of the stereo image to be evaluated as {d R (x,y)}, where (x,y) means {I L (x,y)}, {I R (x,y)} and {d R The coordinate position of the pixel point in (x, y)}, 1≤x≤W, 1≤y≤H, W means {I L (x,y)}, {I R (x,y)} and {d R The width of (x,y)}, H means {I L (x,y)}, {I R (x,y)} and {d R (x,y)} height, I L (x,y) means {I L The pixel value of the pixel whose coordinate position is (x, y) in (x, y)...

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 method for evaluating a stereo image vision comfort level based on machine learning. According to the method, the important vision area mask of stereo images is extracted through the saliency map of a right viewpoint image and a right parallax image, a characteristic vector used for reflecting parallax range characteristics and parallax gradient characteristics and a characteristic vector used for reflecting space frequency characteristics are extracted through the important vision area mask, the characteristic vectors of the stereo images are obtained, training is carried out on all the stereo images in a stereo image set through support vector regression, and finally each stereo image in the stereo image set is tested through a support vector regression training model obtained in training to obtain the vision comfort level evaluation predicted value of each stereo image. The method has the advantages that the obtained characteristic vector information of the stereo images has strong stability and can reflect the vision comfort level changing conditions of the stereo images well, and therefore the correlation of objective evaluation conditions and subjective perception is effectively improved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a machine learning-based method for evaluating visual comfort of stereoscopic images. Background technique [0002] With the rapid development of stereoscopic video display technology and high-quality stereoscopic video content acquisition technology, the quality of experience (QoE, quality of experience) of stereoscopic video is an important issue in the design of stereoscopic video systems, and visual comfort (VC, visual comfort) is an important factor affecting the QoE of stereoscopic video. At present, research on the quality evaluation of stereoscopic video / image mainly considers the influence of content distortion on image quality, but seldom considers the influence of factors such as visual comfort. Therefore, in order to improve the visual experience quality of viewers, it is very important to study the objective evaluation model of visual comfort of stereoscopic vid...

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
IPC IPC(8): H04N17/00H04N13/00
Inventor 邵枫姜求平蒋刚毅郁梅李福翠彭宗举
Owner 湖州优研知识产权服务有限公司
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