H. -W. Chang, X. -D. Bi and C. Kai, "Blind Image Quality Assessment by Visual Neuron Matrix," in IEEE Signal Processing Letters, vol. 28, pp. 1803-1807, 2021, doi: 10.1109/LSP.2021.3106579. Blind image quality assessment by visual neuron matrix(VNM-NN)
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Plase use the citation provided below if it is useful to your research:
1）Hua-wen Chang, Xiao-Dong Bi and Chen Kai, "Blind image quality assessment by visual neuron matrix", IEEE Signal Processing Letters 2）Hua-wen Chang, Xiao-Dong Bi and Chen Kai, "VNM_main", URL:https://github.com/Xiaodong-Bi/VNM
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======================================================================= Author: Hua-wen Chang Version: 1.0 (June 6 2021)
The authors are with the School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China Kindly report any suggestions or corrections to changhuawen@gmail.com
This package contains a Matlab implementation of the Blind image quality assessment by visual neuron matrix(VNM-NN).
Running on Matlab
Input : A test image loaded in an array
Output: The quality scores are between 0 and 1, where 1 represents the same quality as the reference image.
Usage: For quality evaluation, you can just run 'DemoTest.m' as follows:
1、Load the visual neuron matrix and network load('VNM.mat'); % load the feature detector (a matrix of size 128*768 generated by running TrainW(10000,16,16)) load('net.mat'); % load the network
2、Load the image, for example Id = imread('1-1.png');
3、Extract the image features Features=VNM_NN(Id, VNM); %VNM represents the trained visual neuron matrix
4、Use the network to obtain the quality score Score = sim(net,Features) % net and B represent network and feature respectively
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Moreover, this package provides testing of the algorithm on CSIQ database. You can download the database from: CSIQ http://vision.okstate.edu/?loc=csiq
1、Load database information and visual neuron matrix
load('CSIQ.mat'); % load database information and DMOS data
load('VNM.mat'); % load the feature detector, Visual Neuron Matrix VNM
2、Extract image features from CSIQ database 3、Ada-boosting training network was used，the distorted images were divided into two parts with the 80%-20% train-test ratio 4、The final results show PLCC, SRCC, and KROCC results in the CSIQ database, as well as a scatter plot of the test images.
If you have any problems, questions, suggestions, or modifications, please contact me: changhuawen@gmail.com