jpeg.rar_JPEG图像压缩_jpeg matlab_哈弗曼_哈弗曼编码_游程

Ip=imread('lenah1.bmp'); %装入原始图象 figure(1); imshow(Ip) title('原始图象') %显示原图 I=double(Ip); %将数据类型转换为双精度形式 T=dctmtx(8); %产生8*8的DCT变换系数矩阵 B=blkproc(I,[8 8],'P1*x*P2',T,T'); %对图象I的每个8*8块的DCT变换 Q=[16 11 10 16 24 40 51 61 12 12 14 19 26 58 60 55 14 13 16 24 40 57 69 56 14 17 22 29 51 87 80 60 18 22 37 56 68 109 103 77 24 25 55 64 81 104 113 92 49 64 78 87 103 121 120 101 72 92 95 98 112 100 103 99]; %标准量化矩阵 B1=blkproc(B,[8 8],'round(x./P1)',Q);%对DCT系数矩阵进行有损量化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Z扫描 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% sx=[1 2 6 7 15 16 28 29 3 5 8 14 17 27 30 43 4 9 13 18 26 31 42 44 10 12 19 25 32 41 45 54 11 20 24 33 40 46 53 55 21 23 34 39 47 52 56 61 22 35 38 48 51 57 60 62 36 37 49 50 58 59 63 64]; out=zeros([1 32*32*64]); for a=1:32 for b=1:32 for i=1:8 for j=1:8 out(((a-1)*32+b)*64-64+sx(i,j))=B1(a*8-8+i,b*8-8+j); end end end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 稀疏矩阵 游程编码 %%%%%%%%%%%%%%%%%%%%%%%%%%%% r=1; zero_num=0; for i=1:65536 if out(i)~=0 YC(r,1)=zero_num; YC(r,2)=out(i); r=r+1; zero_num=0; else zero_num=zero_num+1; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Huffman编码 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%% 对游程编码后的第一分量(0游程的个数)进行 Huffman编码(1) %%%%%%%%%%% YC1=YC(:,1);[x,y]=size(YC) sig=zeros([max(x,y),3]); k=1; sig(1,1)=YC1(1); for i=1:max(x,y) flag=0; for j=1:k if sig(j,1)==YC1(i) sig(j,2)=sig(j,2)+1; flag=1; break end end if flag==0 k=k+1; sig(k,1)=YC1(i); sig(k,2)=1; end end sig=sig(1:k,:); SUM=sum(sig(:,2)); sig(:,3)=sig(:,2)/SUM; [dict1,avglen1] = huffmandict(sig(:,1),sig(:,3)); % Create dictionary. actualsig1 = YC1; % actual data comp1 = huffmanenco(actualsig1,dict1); % Encode the data. %%% 对游程编码后的第二个分量(幅度)进行 Huffman编码(2) %%%%%%%%%%% YC2=YC(:,2);[x,y]=size(YC) sigg=zeros([max(x,y),3]); k=1; sigg(1,1)=YC2(1); for i=1:max(x,y) flag=0; for j=1:k if sigg(j,1)==YC2(i) sigg(j,2)=sigg(j,2)+1; flag=1; break end end if flag==0 k=k+1; sigg(k,1)=YC2(i); sigg(k,2)=1; end end sigg=sigg(1:k,:); SUM=sum(sigg(:,2)); sigg(:,3)=sigg(:,2)/SUM; [dict2,avglen2] = huffmandict(sigg(:,1),sigg(:,3)); % Create dictionary. actualsig2 = YC2; % actual data comp2 = huffmanenco(actualsig2,dict2); % Encode the data. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Huffman 解码 %%%%%%%%%%%%%%%%%%%%%%%%%%%% dsig1 = huffmandeco(comp1,dict1); % 对游程编码后的第一分量(0游程的个数)进行 Huffman解码(1) dsig2 = huffmandeco(comp2,dict2); % 对游程编码后的第二分量(幅度)进行 Huffman解码(2) %%%%%% 合并成原始游程编码序列 YCC %%%%%% [x,y]=size(dsig1); YCC=zeros([max(x,y),2]); YCC(:,1)=dsig1; YCC(:,2)=dsig2; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 稀疏矩阵 游程解码 %%%%%%%%%%%%%%%%%%%%%%%%%%%% out1=zeros([1 32*32*64]); [x,y]=size(YCC); t=1; for i=1:x if YCC(i,1)==0 out1(t)=YCC(i,2); else t=t+YCC(i,1); out1(t)=YCC(i,2); end t=t+1; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 反Z扫描 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for a=1:32 for b=1:32 for i=1:8 for j=1:8 B11(a*8-8+i,b*8-8+j)=out1(((a-1)*32+b)*64-64+sx(i,j)); end end end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% B2=blkproc(B11,[8 8],'x.*P1',Q); %对DCT系数矩阵进行反量化 B3=blkproc(B2,[8 8],'P1*x*P2',T',T); %进行反DCT变化后重构原图 B4=uint8(B3); %转换数据类型 figure(2); imshow(B4) title('压缩后的图象') %显示压缩后的图像 %%%%%%%%%%%%%%%%%%%%%%%% 计算峰值信噪比 PSNR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% p=(Ip-B4).^2; h=sum(p); h1=sum(h'); mse=h1/256/256; psnr=10*log10(255*255/mse) %运行结果 psnr =36.3820