資源簡介
針對結(jié)構(gòu)相似度(Structural SIMilarity, SSIM)和其他一些方法的局限性, 考慮到梯度可以反映圖像的邊緣紋理等結(jié)構(gòu)信息, 提出了一種快速的全參考型IQA算法,即提升的梯度加權(quán)結(jié)構(gòu)相似度(Gradient Weighted Lifting Structural SIMilarity, GWL-SSIM)方法.
代碼片段和文件信息
function?GWL_SSIM?=?GWL_SSIM(imageRef?imageDis)
%Input?:?(1)?imageRef:?the?first?image?being?compared
%????????(2)?imageDis:?the?second?image?being?compared
%
%Output:?(1)?GWL_SSIM:?is?the?similarty?score?calculated?using?GWL_SSIM?algorithm.?GWL_SSIM
% ?????only?considers?the?luminance?component?of?images.???????
%-----------------------------------------------------------------------
%
%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.
%?GWL_SSIM?=?GWL_SSIM(img1?img2);
%-----------------------------------------------------------------------
[rows?cols?junk]?=?size(imageRef);
if?junk?==?3??%images?are?colorful
????Y1?=?0.299?*?double(imageRef(::1))?+?0.587?*?double(imageRef(::2))?+?0.114?*?double(imageRef(::3));
????Y2?=?0.299?*?double(imageDis(::1))?+?0.587?*?double(imageDis(::2))?+?0.114?*?double(imageDis(::3));
else?%images?are?grayscale
????Y1?=?double(imageRef);
????Y2?=?double(imageDis);
end
Y1?=?double(Y1);
Y2?=?double(Y2);
%%%%%%%%%%%%%%%%%%%%%%%%%%?Download?the?image?%%%%%%%%%%%%%%%%%%%%%%%%%
minDimension?=?min(rowscols);
F?=?max(1round(minDimension?/?256));?
aveKernel?=?fspecial(‘a(chǎn)verage‘F);?
aveY1?=?conv2(Y1?aveKernel‘same‘);?
aveY2?=?conv2(Y2?aveKernel‘same‘);?
Y1?=?aveY1(1:F:rows1:F:cols);?
Y2?=?aveY2(1:F:rows1:F:cols);?
%%%%%%%%%%%%%%%%%%%%%%%%%%?Calculate?the?gradient?map%%%%%%%%%%%%%%%%%%%%%%%%%
dx?=?[1?0?-1;?1?0?-1;??1??0?-1]/3;
dy?=?[1?1?1;?0??0???0;?-1?-1?-1]/3;%Prewitt?operators
p=0.5;
IxY1?=?conv2(Y1?dx?‘same‘);?????
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