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

Image quality evaluation method based on combination neural network and classification neural network

An image quality evaluation and neural network technology, applied in the field of image quality evaluation, can solve problems such as inability to obtain original images and insufficient pertinence

Active Publication Date: 2018-10-16
上海皓云文化传播有限公司
View PDF6 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Objective image quality evaluation methods are mainly divided into three evaluation methods, namely, full-reference image quality evaluation method, semi-reference image quality evaluation method and no-reference image quality evaluation method. The corresponding original image cannot be obtained in the application, so the research of no reference image quality evaluation method is more practical
[0004] The existing general-purpose non-reference image quality evaluation methods are mainly for multiple distortions, and the pertinence for a specific distortion is not strong enough. Therefore, the various distortions are first classified, and then for a specific Image quality evaluation based on distortion has become a direction that people pay more and more attention to

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
  • Image quality evaluation method based on combination neural network and classification neural network
  • Image quality evaluation method based on combination neural network and classification neural network
  • Image quality evaluation method based on combination neural network and classification neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0042] An image quality evaluation method based on combined neural network and classification neural network proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes two processes of training phase and testing phase;

[0043] The specific steps of the described training phase process are:

[0044] Step ①_1: Select P original undistorted images, and record the pth original undistorted image as Among them, P is a positive integer, P>1, if P=100, p is a positive integer, 1≤p≤P, 1≤x≤W, 1≤y≤H, W means The width, H means the height of, express The pixel value of the pixel whose middle coordinate position is (x, y).

[0045] Step ①_2: Utilize the existing jpeg2000 distortion generation method to generate K distorted images of different distortion degrees under the jpeg2000 distorti...

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 image quality evaluation method based on a combination neural network and a classification neural network. In the training stage, an objective reality quality image of a distortion image obtained by adopting a full-reference image quality evaluation method is adopted as supervision, a normalized image of the distortion image is trained to obtain a combination neural network regression training model for different distortion types; a classification label of the distortion image is adopted as supervision, and the normalized image of the distortion image is trained to obtain a classification neural network training model; in the testing stage, the normalized image of the distortion image to be evaluated is input into the classification neural network training model,and a distortion type is obtained; according to the distortion type, the normalized image is input into the corresponding combination neural network regression training model to obtain an objective quality evaluation prediction quality map, and adopting a saliency map for performing weighing pooling on the objective quality evaluation prediction quality map, and obtaining an objective quality evaluation prediction value. The method has the advantage that the correlation between the objective evaluation result and subjective perception is effectively improved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an image quality evaluation method based on a combined neural network and a classification neural network. Background technique [0002] With the rapid development of image processing, machine learning, and computer vision, image quality assessment has become a research field that has attracted more and more attention, because it is an important technique in practical applications and can be used to accurately evaluate the quality of images. In the process of image acquisition, transmission, compression, storage, and display, there are often different degrees of distortion, such as image blur, video terminal image distortion, and image quality in the system are not up to standard. Therefore, an effective image quality evaluation mechanism should be established Very important. [0003] In general, image quality assessment can be roughly divided into two different categories: ...

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): G06T7/00
CPCG06T7/0002G06T2207/20084G06T2207/30168
Inventor 周武杰张爽爽师磊磊潘婷顾鹏笠蔡星宇邱薇薇何成陈芳妮葛丁飞金国英孙丽慧郑卫红李鑫吴洁雯王昕峰施祥翟治年
Owner 上海皓云文化传播有限公司
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