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‎CalMSE.m

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% Calculate the MSE
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function MSE = CalMSE(orig_image, reco_image)
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clear dist_image;
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dist_image = orig_image - reco_image;
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sum_image = dist_image.*dist_image;
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sumdist = sum(sum(sum(sum(sum_image))));
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[rownum, colnum, dimension, imagenum] = size(orig_image);
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MSE = sumdist/(rownum*colnum*dimension*imagenum);
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‎Compratio.m

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% Calculate the compression ratio
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% between the original images and the compressed images;
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function comp_ratio = Compratio(orig_image, comp_image)
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% Calculate how many bits should be used to represented the original images
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% and store it in the variable B0
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clear tempmatr1;
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tempmatr1 = ceil(log2(orig_image+1));
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clear sizevector1;
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sizevector1 = size(orig_image);
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[rownum, colnum] = size(sizevector1);
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while colnum >1
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clear tempmatr2;
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tempmatr2 = sum(tempmatr1);
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clear tempmatr1;
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tempmatr1 = tempmatr2;
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colnum = colnum -1;
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end
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B0 = sum(tempmatr1);
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% Calculate how many bits should be used to represented the compressed images
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% and store it in the variable B1
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clear tempvec1;
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tempvec1 = find(comp_image<0);
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clear tempmatr1;
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if sum(tempvec1) == 0
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tempmatr1 = ceil(log2(comp_image+1));
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else
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tempmatr1 = ceil(log2(abs(comp_image)+1))+1;
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end
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clear sizevector1;
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sizevector1 = size(comp_image);
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[rownum, colnum] = size(sizevector1);
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while colnum >1
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clear tempmatr2;
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tempmatr2 = sum(tempmatr1);
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clear tempmatr1;
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tempmatr1 = tempmatr2;
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colnum = colnum -1;
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end
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B1 = sum(tempmatr1);
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comp_ratio = B0/B1;

‎Compress.m

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% Image compression funtion
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function [comp_image_Y,comp_image_U,comp_image_V] = Compress(orig_image)
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RGB=orig_image;
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%下面是对RGB三个分量进行分离
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R=RGB(:,:,1);
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G=RGB(:,:,2);
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B=RGB(:,:,3);
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%RGB->YUV
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Y=0.299*double(R)+0.587*double(G)+0.114*double(B);
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[xm, xn] = size(Y);
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U=-0.169*double(R)-0.3316*double(G)+0.5*double(B);
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V=0.5*double(R)-0.4186*double(G)-0.0813*double(B);
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%产生一个8*8的DCT变换举证
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T=dctmtx(8);
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%进行DCT变换 BY BU BV是double类型
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BY=blkproc(Y,[8 8],'P1*x*P2',T,T');
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BU=blkproc(U,[8 8],'P1*x*P2',T,T');
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BV=blkproc(V,[8 8],'P1*x*P2',T,T');
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%低频分量量化表
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a=[
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16 11 10 16 24 40 51 61;
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12 12 14 19 26 58 60 55;
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14 13 16 24 40 57 69 55;
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14 17 22 29 51 87 80 62;
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18 22 37 56 68 109 103 77;
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24 35 55 64 81 104 113 92;
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49 64 78 87 103 121 120 101;
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72 92 95 98 112 100 103 99;
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];
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%高频分量量化表
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b=[17 18 24 47 99 99 99 99;
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18 21 26 66 99 99 99 99;
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24 26 56 99 99 99 99 99;
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47 66 99 99 99 99 99 99;
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99 99 99 99 99 99 99 99;
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99 99 99 99 99 99 99 99;
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99 99 99 99 99 99 99 99;
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99 99 99 99 99 99 99 99;];
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%使用量化表对三个分量进行量化
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BY2=blkproc(BY,[8 8],'round(x./P1)',a);
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BU2=blkproc(BU,[8 8],'round(x./P1)',b);
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BV2=blkproc(BV,[8 8],'round(x./P1)',b);
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%调用压缩函数
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comp_image_Y=img2jpg(BY2,1);
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comp_image_U=img2jpg(BU2,2);
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comp_image_V=img2jpg(BV2,3);

‎Decompress.m

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function reco_image = Decompress(orig_image_Y,orig_image_U,orig_image_V)
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%解压缩
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YI=jpg2img(orig_image_Y);
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UI=jpg2img(orig_image_U);
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VI=jpg2img(orig_image_V);
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%YUV转为RGB
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RI=YI-0.001*UI+1.402*VI;
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GI=YI-0.344*UI-0.714*VI;
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BI=YI+1.772*UI+0.001*VI;
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%经过DCT变换和量化后的YUV图像
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RGBI=cat(3,RI,GI,BI);
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RGBI=uint8(RGBI);
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reco_image = RGBI;

‎Image1.bmp

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‎Image2.bmp

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‎Image3.bmp

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‎Image4.bmp

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‎Projec1.jpg

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‎Projec2.jpg

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