【图像评价】基于Gabor特征的屏幕内容图像质量评估模型matlab代码

1 简介本文公开了一种基于Gabor特征互信息的全参考图像质量评价方法,属于图像处理领域.本发明方法具体实施包括如下步骤:(一)输入参考图像和失真图像;(二)对参考图像和失真图像,分别利用二维Gabor滤波器进行滤波以提取Gabor特征;(三)分别建立概率分布函数,求出滤波后图像每一个像素点的概率分布及联合概率分布;(四)求出参考图像和失真图像Gabor特征的互信息;(五)建立图像质量评价函数MIGF评价图像质量.本文利用二维Gabor滤波器提取视觉特征,并通过参考图像和失真图像的互信息来建立图像质量评价函数,评价结果符合人类视觉主观认识.2 部分代码%% Demo%----------------------------------------------------------------------%% This is an implementation of the algorithm for calculating the% Gabor features-basedmodel (GFM) index between two images.%% Please refer to the following paper%% Z. Ni, H. Zeng, L. Ma, J. Hou, J. Chen, and K.-K. Ma, “A Gabor% Feature-based Quality Assessment Model for the Screen Content Images,”% IEEE Transactions on Image Processing, 2018.%----------------------------------------------------------------------clcclearcloseorigImg imread(.\SCI07.bmp);distImg imread(.\SCI07_2_4.bmp);fprintf(Image load finished. \n)[gfm, gfm_map] GFM(origImg, distImg);fprintf(The score of the distorted image is %.4f \n, gfm)function [score, SimMatrix] GFM(Im1,Im2)% % GFM Index for SCI, Version 1.0% Copyright(c) 2017 Zhangkai Ni, Huanqiang Zeng, Lin Ma, Junhui Hou,% Jing Chen, and Kai-Kuang Ma% All Rights Reserved.%% ----------------------------------------------------------------------% Permission to use, copy, or modify this software and its documentation% for educational and research purposes only and without fee is here% granted, provided that this copyright notice and the original authors% names appear on all copies and supporting documentation. This program% shall not be used, rewritten, or adapted as the basis of a commercial% software or hardware product without first obtaining permission of the% authors. The authors make no representations about the suitability of% this software for any purpose. It is provided as is without express% or implied warranty.%----------------------------------------------------------------------%% This is an implementation of the algorithm for calculating the% Gabor features-basedmodel (GFM) index between two images.%% Please refer to the following paper%% Z. Ni, H. Zeng, L. Ma, J. Hou, J. Chen, and K.-K. Ma, “A Gabor% Feature-based Quality Assessment Model for the Screen Content Images,”% IEEE Transactions on Image Processing, 2018.%%----------------------------------------------------------------------%%Input : (1) Im1: the first image being compared, which is a RGB image% (2) Im2: the second image being compared, which is a RGB image%%Output: (1) score: is the similarty score calculated using GFM algorithm.% GFM considers the luminance component and chrominance component of images.%% (2) SimMatrix: is the local quality map of the distorted image%%-----------------------------------------------------------------------%%Usage:%Given 2 test images img1 and img2. For gray-scale images, their dynamic range should be 0-255.%For colorful images, the dynamic range of each color channel should be 0-255.%%[score, SimMatrix] GFM(img1, img2);%-----------------------------------------------------------------------%%%%% Transform into an opponent color spaceL1 0.06 * double(Im1(:,:,1)) 0.63 * double(Im1(:,:,2)) 0.27 * double(Im1(:,:,3));L2 0.06 * double(Im2(:,:,1)) 0.63 * double(Im2(:,:,2)) 0.27 * double(Im2(:,:,3));M1 0.30 * double(Im1(:,:,1)) 0.04 * double(Im1(:,:,2)) - 0.35 * double(Im1(:,:,3));M2 0.30 * double(Im2(:,:,1)) 0.04 * double(Im2(:,:,2)) - 0.35 * double(Im2(:,:,3));N1 0.34 * double(Im1(:,:,1)) - 0.60 * double(Im1(:,:,2)) 0.17 * double(Im1(:,:,3));N2 0.34 * double(Im2(:,:,1)) - 0.60 * double(Im2(:,:,2)) 0.17 * double(Im2(:,:,3));%%%%%%%%%%%%%%%%To 330;Tc 100;lambda 0.04;3 仿真结果4 参考文献[1]丁勇等. 基于Gabor特征互信息的全参考图像质量评价方法. CN, CN102497576 B.