Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

DDBTC_Encode.m

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+function [xMin, xMax, imageBitmap] = DDBTC_Encode(inputImage, blockSizeX, blockSizeY, classMatrix, diffusedMatrix);
+inputImage = double(inputImage);
+[row, col] = size(inputImage);
+
+numberOfBlockX = row / blockSizeX;
+numberOfBlockY = col / blockSizeY;
+
+inputImage = mat2cell(inputImage, blockSizeX*ones(1, numberOfBlockX), blockSizeY*ones(1, numberOfBlockY));
+imageBitmap = zeros(row, col);
+imageBitmap = mat2cell(imageBitmap, blockSizeX*ones(1, numberOfBlockX), blockSizeY*ones(1, numberOfBlockY));
+xMin = zeros(numberOfBlockX, numberOfBlockY);
+xMax = zeros(numberOfBlockX, numberOfBlockY);
+
+for i=1: numberOfBlockX
+    for j=1: numberOfBlockY
+        pixel = inputImage{i, j};
+        [xMin(i, j), xMax(i, j), imageBitmap{i, j}] = PerformErrorDiffusionHalftoning(pixel, classMatrix, diffusedMatrix);
+    end;
+end;
+
+imageBitmap = cell2mat(imageBitmap);
+
+
+function [xMin, xMax, imageBitmap] = PerformErrorDiffusionHalftoning(pixel, classMatrix, diffusedMatrix);
+[row, col] = size(pixel);
+[classMatrixSizeX, classMatrixSizeY] = size(classMatrix);
+
+xMin = min(pixel(:)); 
+xMax = max(pixel(:)); 
+xMean = mean(pixel(:));
+
+r = 1;
+x = padarray(pixel, [r r], 'symmetric');
+paddedClassMatrix = padarray(classMatrix, [r r], 'symmetric');
+
+imageBitmap = zeros(row, col);
+
+for processingOrder = 0: classMatrixSizeX*classMatrixSizeY-1
+    [positionOrderX, positionOrderY] = find(classMatrix == processingOrder);
+
+    if x(positionOrderX+r, positionOrderY+r) < xMean
+        imageBitmap(positionOrderX, positionOrderY) = 0;
+        o = xMin;
+    else
+        imageBitmap(positionOrderX, positionOrderY) = 1;
+        o = xMax;
+    end;
+
+    e = x(positionOrderX+r, positionOrderY+r) - o;
+    unProcessedPixel = (paddedClassMatrix(positionOrderX+r-r: positionOrderX+r+r, positionOrderY+r-r: positionOrderY+r+r) >= processingOrder);
+    x(positionOrderX+r-r: positionOrderX+r+r, positionOrderY+r-r: positionOrderY+r+r) = x(positionOrderX+r-r: positionOrderX+r+r, positionOrderY+r-r: positionOrderY+r+r) + e*(unProcessedPixel.*diffusedMatrix)/sum(sum(unProcessedPixel.*diffusedMatrix));
+end;

DDbtc.m

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+function [H]=DDbtc1(in,met,bs)
+%in–> input image, bs-block size(2,4,..)
+in=im2double(in);
+[s1 s2]=size(in);
+inpad=padarray(in,[1,1],'both');
+% inpad_hist=0;
+
+if(met==1)
+
+              CM = [47 31 51 24 27 45 5 21;
+                    37 63 53 11 22 4 1 33;
+                    61 0 57 16 26 29 46 8;
+                    20 14 9 62 18 41 38 6;
+                    17 13 25 15 55 48 52 58;
+                    3 7 2 32 30 34 56 60;
+                    28 40 36 39 49 43 35 10;
+                    54 23 50 12 42 59 44 19;];  %Messe-Vaidhya
+                    kr=8;nn=63;
+elseif(met==2)
+
+               CM=  [34 48 40 32 29 15 23 31;    
+                     42 58 56 53 21 5 7 10;
+                     50 62 61 45 13 1 2 18;
+                     38 46 54 37 25 17 9 26;
+                     28 14 22 30 35 49 41 33;
+                     20 4 6 11 43 59 57 52;
+                     12 0 3 19 51 63 60 44;
+                     24 16 8 27 39 47 55 36;]; %Knuth
+                     kr=8;nn=63;
+else
+
+   CM= [207 0 13 17 28 55 18 102 81 97 74 144 149 169 170 172;
+        3 6 23 36 56 50 65 87 145 130 137 158 182 184 195 221;
+        7 14 24 37 67 69 86 5 106 152 150 165 183 192 224 1;
+        15 26 43 53 51 101 115 131 139 136 166 119 208 223 226 4;
+        22 39 52 71 84 103 164 135 157 173 113 190 222 225 227 16;
+        40 85 72 83 104 117 167 133 168 180 200 219 231 228 12 21;
+        47 120 54 105 123 132 146 176 179 202 220 230 245 2 20 41;
+        76 73 127 109 138 134 178 181 206 196 229 244 246 19 42 49;
+        80 99 112 147 142 171 177 203 218 232 243 248 247 33 48 68;
+        108 107 140 143 185 163 204 217 233 242 249 255 44 45 70 79;
+        110 141 88 75 175 205 214 234 241 250 254 38 46 77 116 100;
+        111 148 160 174 201 215 235 240 251 252 253 61 62 93 94 125;
+        151 159 189 199 197 216 236 239 25 31 60 82 92 95 124 114;
+        156 188 191 209 213 237 238 29 32 59 64 91 118 78 128 155;
+        187 194 198 212 9 10 30 35 58 63 90 96 122 129 154 161;
+        193 210 211 8 11 27 34 57 66 89 98 121 126 153 162 186];
+        kr=16;nn=255;
+
+end
+
+CM=repmat(CM,round(bs/kr),round(bs/kr));
+
+%Diffusion Matrix
+
+   DM=[1 2 1;
+       2 0 2;
+       1 2 1;]/12;
+
+%    CM1=zeros(s1+2,s2+2)-1;
+%    CM1(2:s1+1,2:s1+1)=CM;
+%    CM=CM1;
+   CM=padarray(CM,[1 1],-1,'both');
+   H=zeros(s1,s2);     
+
+for i=1:bs:s1
+for j=1:bs:s2
+    block=in(i:i+bs-1,j:j+bs-1);
+    mn=mean(block(:));
+    a=max(block(:));
+    b=min(block(:)); 
+%     i1=1; j1=1;
+    for ii=0:1:nn
+        [p1,p2]=(find(CM==ii));
+        y=1;
+        x=1;
+        while y<=size(p1,1)
+        while x<=size(p2,1)
+            inpix=inpad(i-1+p1(y),j-1+p2(x));
+            if inpix>=mn
+                out=a;
+            else
+                out=b;
+            end
+            H(i-1+p1(y),j-1+p2(x)) = out;
+            qerr=inpix-out;
+            cl=CM(p1(y)-1:p1(y)+1,p2(x)-1:p2(x)+1);
+            k=cl>ii;
+            DM1=(DM.*k);
+            DM1=DM/sum(DM1(:));
+            err=DM1*qerr;
+
+            inpad(p1(y)-1:p1(y)+1,p2(x)-1:p2(x)+1)= inpad(p1(y)-1:p1(y)+1,p2(x)-1:p2(x)+1)+err;
+
+            y=y+1;
+            x=x+1;
+        end
+        end
+    end
+
+end
+end
+
+H=H(2:(s1+1),2:(s2+1));
+
+% in1=imgaussfilt(in,1.3);
+% H1=imgaussfilt(H,1.3);
+% 
+% fprintf('\n');
+% [peaksnr, snr] = psnr(in1, H1);
+% fprintf('\n The H-Peak-SNR-1 value is %0.4f. \n', peaksnr);
+% [peaksnr, snr] = psnr(in, H);
+% fprintf('\n The Peak-SNR value is %0.4f. \n', peaksnr);
+% imshow(in),figure,imshow(H),figure,imshow(H1);
+
+% imwrite(H,'lena_DDbtc_64.jpg');
+
+in=im2uint8(in);
+H=im2uint8(H);
+hp=HPSNRnew(in,H,bs,1.3);
+end

EDbtc.m

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+function [out]=EDbtc(in,bs)
+%in–> input image, bs-block size(2,4,..)
+in=im2double(in);
+fc=[0 -99*16 7; 3 5 1]/16; 
+
+[ri,ci]=size(in);
+[rm,cm]=size(fc);
+[r0,c0]=find(fc==-99);    % find origin of error filter
+fc(r0,c0)=0;
+
+rm=rm-1; cm=cm-1;
+inpad=padarray(in,[0 1],'both');
+inpad=padarray(inpad,[1],'post');
+out=zeros(ri,ci); qn=out;
+
+for i=r0:bs:ri+(r0-1)
+for j=c0:bs:ci+(c0-1)
+    i1=i-(r0-1);
+    j1=j-(c0-1);
+    block=in(i1:i1+bs-1,j1:j1+bs-1);
+    mn=mean(block(:));
+    a=max(block(:));
+    b=min(block(:));
+
+    for y=i:i+bs-1
+    for x=j:j+bs-1
+        y1=y-(r0-1);
+        x1=x-(c0-1);
+
+        if inpad(y,x)>=mn
+            outpix=a;
+        else
+            outpix=b;
+        end    
+        out(y1,x1)=outpix;
+        qerr=inpad(y,x)-outpix;
+        qn(y1,x1)=qerr;
+        qerr=qerr.*fc;
+        bl_inpad=inpad(y1:y1+rm,x1:x1+cm);
+        bl_inpad=bl_inpad+qerr;
+        inpad(y1:y1+rm,x1:x1+cm)=bl_inpad;
+    end     
+    end
+
+end
+end
+
+% in1=imgaussfilt(in,1.3);
+% out1=imgaussfilt(out,1.3);
+% 
+% fprintf('\n');
+% [peaksnr, snr] = psnr(in, out);
+% fprintf('\n The Peak-SNR value is %0.4f. \n', peaksnr);
+% [peaksnr, snr] = psnr(in1, out1);
+% fprintf('\n The H-Peak-SNR value is %0.4f. \n', peaksnr);
+% imshow(in),figure,imshow(out),figure,imshow(out1);
+
+% imwrite(out,'lena_EDbtc_64.jpg');
+
+in=im2uint8(in);
+out=im2uint8(out);
+hp=HPSNRnew(in,out,bs,1.3);
+end
+
+

HPSNRnew.m

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+%Function to compute HPSNR
+
+function hp=HPSNRnew(in,in1,fs,d)
+%fs==> Block Size
+%d==> standard deviation
+
+[s1 s2]=size(in);
+ in=double(in);
+ in1=double(in1);
+
+ b1=mod(s1,fs);
+ b2=mod(s2,fs);
+ bs=fs-1;
+ in=padarray(in,[b1 b2]);
+ in1=padarray(in1,[b1 b2]);
+
+%Gaussian Filter
+gaulen=(fs-1)/2; 
+for k=-gaulen:gaulen
+    for l=-gaulen:gaulen
+        c=(k*k + l*l)/(2*d*d);
+        GF(k+gaulen+1,l+gaulen+1)=exp(-c)/(2*3.14*d*d);
+    end
+end
+SErr=0;
+for i=1:fs:s1-1
+    for j=1:fs:s2-1            
+        ima=in(i:i+bs,j:j+bs);
+        imr=in1(i:i+bs,j:j+bs) ;      
+        Err=ima-imr;
+        Err=conv2(Err,GF,'same').^2;
+        SErr=SErr+sum(sum(Err));
+    end
+end
+hp=10*log10(((s1)*(s2)*255.^2)/SErr);
+fprintf('\n The H-Peak-SNR value is %0.4f. \n', hp);
+end
+
+
+
+

ODbtc.m

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+function [H]=ODbtc(in,bs)
+%in–> input image, bs-block size(2,4,..)
+in=im2double(in);
+[s1 s2]=size(in);
+
+
+%Ordered Dithering - Halftoning
+od=[1 17 5 21 2 18 6 22;
+  25 9 29 13 26 10 30 14;
+  7 23 3 19 8 24 4 20;
+  31 15 27 11 32 16 28 12;
+  2 18 6 22 1 17 5 21;
+  26 10 30 14 25 9 29 13;
+  8 24 4 20 7 23 3 19;
+  32 16 28 12 31 15 27 11]/32;
+
+for a=1:8
+for b=1:8
+    od_temp=(od(a,b)-min(od(:)))/(max(od(:))-min(od(:)));
+    od_new(a,b)=od_temp;
+end
+end
+
+od_new=repmat(od_new,round(s1/8),round(s2/8));
+
+%BTC
+for i=1:bs:s1
+for j=1:bs:s2
+bl=in(i:i+(bs-1),j:j+(bs-1));
+
+a=max(bl(:));
+b=min(bl(:));
+
+k=(a-b);
+
+for m=i:1:i+bs-1
+for n=j:1:j+bs-1
+    DA = k*od_new(m,n);
+    th = DA + b;
+    c=in(m,n)>th;
+    out=((c).*a+((~c)).*b);
+    H(m,n)=out;
+end
+end
+
+end
+end
+
+% in1=imgaussfilt(in,1.3);
+% H1=imgaussfilt(H,1.3);
+
+% [peaksnr, snr] = psnr(in, H);
+% fprintf('\n The Peak-SNR value is %0.4f. \n', peaksnr);
+% [peaksnr, snr] = psnr(in1, H1);
+% fprintf('\n The H-Peak-SNR value is %0.4f. \n', peaksnr);
+% imshow(in),figure,imshow(H);
+
+% imwrite(H,'lena_ODbtc_64.jpg');
+
+in=im2uint8(in);
+H=im2uint8(H);
+hp=HPSNRnew(in,H,bs,1.3);
+end

ambtc.m

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+function [H]=ambtc(in,bs)
+%in–> input image; bs–> blocksize
+
+in=rgb2gray(in);
+in=im2double(in);
+[s1 s2]=size(in);
+n=bs*bs;
+for i=1:bs:s1
+for j=1:bs:s2
+bl=in(i:i+(bs-1),j:j+(bs-1));
+mn=mean(mean(bl)) ; %Computing Mean
+fm=mean(mean(abs(bl-mn))) ; %Abs moment
+g=(n*fm)/2;
+c=bl>mn;
+q=nnz(c);
+if(q==n)
+b=mn;
+a=0;
+else
+b=mn+(g/q);
+a=mn-(g/(n-q));
+end
+out=round(c.*b+(~c).*a);
+H(i:i+(bs-1),j:j+(bs-1))=out;
+end
+end
+imshow(H);
+end