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

A No-reference Stereo Image Quality Objective Evaluation Method Based on Machine Learning

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

Active Publication Date: 2016-08-24
郎溪品旭科技发展有限公司
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is currently no effective objective evaluation method for stereoscopic image quality

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 Stereo Image Quality Objective Evaluation Method Based on Machine Learning
  • A No-reference Stereo Image Quality Objective Evaluation Method Based on Machine Learning
  • A No-reference Stereo Image Quality Objective Evaluation Method Based on Machine Learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0046] A kind of no-reference stereoscopic image quality objective evaluation method based on machine learning proposed by the present invention, its overall realization block diagram is as follows figure 1 shown, which includes the following steps:

[0047] ① Select N original undistorted stereoscopic image left view images to form a training image set, denoted as {L i,org |1≤i≤N}, where, N≥1, L i,org means {L i,org The i-th image in |1≤i≤N} represents the left-viewpoint image of the i-th original undistorted stereo image, and the symbol "{}" is a set representation symbol.

[0048] During specific implementation, the number of frames selected for the original undistorted stereoscopic image should be appropriate. If the value of N is larger, the accuracy of the visual dictionary table obtained through training is also higher, but the compu...

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 reference-free three-dimensional picture quality objective evaluation method based on machine learning. The reference-free three-dimensional picture quality objective evaluation method comprises the steps that a vision dictionary list is structured in a training stage, sparse coefficient matrixes of all subblocks in a left viewpoint picture and all subblocks in a right viewpoint picture are calculated respectively for the left view point picture and the right view point picture of any distortion three-dimensional picture according to the structured vision dictionary list, the characteristic vector of the left viewpoint picture and the characteristic vector of the right viewpoint picture are obtained through the maximum pooling method, characteristic extraction is simple, and the computation complexity is low; a support vector regression training model of the left viewpoint picture and a support vector regression training model of the right viewpoint picture are structured, objective quality evaluation predictive values, corresponding to each characteristic vector in a test sample data set, of the left viewpoint picture and the right viewpoint picture respectively are obtained through prediction, weighing is conducted according to characteristic vector information, the objective quality evaluation prediction value of the three-dimensional picture is obtained, and the correlation between the objective evaluation result and subjective perception is high.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a machine learning-based objective evaluation method for stereoscopic image quality without reference. Background technique [0002] With the rapid development of image coding technology and stereoscopic display technology, stereoscopic image technology has received more and more attention and applications, and has become a current research hotspot. Stereoscopic image technology utilizes the principle of binocular parallax of the human eye. Both eyes independently receive left and right viewpoint images from the same scene, and form binocular parallax through brain fusion, so as to enjoy stereoscopic images with a sense of depth and realism. . Compared with single-channel images, stereo images need to ensure the image quality of two channels at the same time, so it is very important to evaluate its quality. However, there is currently no effective objective evaluation metho...

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): H04N17/00H04N19/154H04N13/00
Inventor 邵枫李柯蒙李福翠
Owner 郎溪品旭科技发展有限公司
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