last changes motion estimation and stereoscopy

Author: yolanda93

Motion-Estimation/Block_Matching.m

@@ -28,7 +28,7 @@
             continue;
         else
             new_block =new_frame(new_xpos:new_xpos+(block_size-1),new_ypos:new_ypos+(block_size-1));
-            MSE(m+p+1,n+p+1)=double(mean2(((reference_block-new_block).^2)));
+            MSE(m+p+1,n+p+1)=double(mean2(((double(reference_block)-double(new_block)).^2)));
         end
     end
 end

Motion-Estimation/Motion_Compensation.m

@@ -21,7 +21,7 @@
         dx = motion_vect(2,vect_count);
         y_block = i + dy;
         x_block = j + dx;
-        if(y_block>=1&&x_block>=1&&dy~=0&&dx~=0)      
+        if(y_block>=1&&x_block>=1&&dy~=0&&dx~=0&& (y_block+block_size-1<size(reference_frame,1) && x_block+block_size-1<size(reference_frame,2)))      
             image_compensation(i:i+block_size-1,j:j+block_size-1) = reference_frame(y_block:y_block+block_size-1, x_block:x_block+block_size-1);
         end
         vect_count = vect_count + 1;

Motion-Estimation/Motion_Estimation.m

@@ -12,7 +12,7 @@
 %
 
 [rows, cols] = size(reference_frame);
-motion_vectors = zeros(3,rows*cols/block_size^2);
+motion_vectors = zeros(3,round(rows*cols/block_size^2));
 
 vect_count = 1;
 for posx = 1:block_size:rows-block_size

Motion-Estimation/draw_Optical_Flow.m

@@ -3,7 +3,7 @@ function draw_Optical_Flow( reference_frame, motion_vect, block_size )
 
 [row, col] = size(reference_frame);
 mbCount = 1;
-subplot(2,2,4),subimage(reference_frame),title('Motion Vectors');
+subplot(2,2,4),subimage(uint8(reference_frame)),title('Motion Vectors');
 hold on;
 for i = 1:block_size:row-block_size
     for j = 1:block_size:col-block_size       

Motion-Estimation/image_PSNR.m

@@ -1,11 +1,11 @@
-function PSNR = image_PSNR(reference_image, estimated_image)
+function PSNR = image_PSNR(new_image, estimated_image)
 
-[row, col] = size(reference_image);
+[row, col] = size(new_image);
 sum_err = 0;
 for i = 1:row
     for j = 1:col
-        sum_err = sum_err + (reference_image(i,j) - estimated_image(i,j))^2;
+        sum_err = sum_err + double(mean2(abs(double(new_image(i,j)) - double(estimated_image(i,j))).^2));
     end
 end
-mse = sum_err/(row*col);
-PSNR = 10*log10(double(255^2/mse));
+
+PSNR = 10*log10(double(255^2/sum_err));

Motion-Estimation/script.m

@@ -9,31 +9,33 @@
 end
 
 % Compute the motion vectors between 2 frames
-reference_frame = imread('../Test-images/Frames/image40.jpg');
+fileName = '../Test-images/Frames/image40.jpg';
+reference_frame = imread(fileName);
 new_frame = imread('../Test-images/Frames/image41.jpg');
 
 % Transform in gray scale for simplification
-reference_frame = rgb2gray(reference_frame);
-new_frame = rgb2gray(new_frame);
+reference_frame = double(rgb2gray(reference_frame));
+new_frame = double(rgb2gray(new_frame));
 
 % Plot the reference and new frames
-subplot(2,2,1),subimage(reference_frame),title('Reference Frame'); hold on;
-subplot(2,2,2),subimage(new_frame),title('New Frame');
+subplot(2,2,1),subimage(uint8(reference_frame)),title('Reference Frame'); hold on;
+subplot(2,2,2),subimage(uint8(new_frame)),title('New Frame');
 
 % p is the search parameter, block_size is the size of the macroblock
-p=7;block_size=16;
+p=6;block_size=2;
 
 % Motion estimation using an exhaustive method
 [motion_vect] = Motion_Estimation(reference_frame,new_frame,block_size,p);
 
 % Motion competation using the motion vectors 
 estimated_frame= Motion_Compensation(reference_frame, motion_vect, block_size);
-subplot(2,2,3),subimage(estimated_frame),title('Estimated Frame');
+subplot(2,2,3),subimage(uint8(estimated_frame)),title('Estimated Frame');
 draw_Optical_Flow( reference_frame, motion_vect, block_size );
 hold off;
 
+
 % Peak Signal-to-Noise Ratio (PSNR) to measure the distortion of the frame which is estimated and the predicted one
-PSNR = image_PSNR(new_frame, estimated_frame);
-fprintf('Peak-Signal-To-Noise Ratio: %d \n',PSNR);
+[PSNR1,mse,~] = measerr(new_frame, estimated_frame)
+
 
 

Stereoscopy/stereoscopy_script.m

@@ -141,13 +141,13 @@
     rightImage_dq(rows_r/2+1:rows_r, cols_r/2+1:cols_r,i) = dequantize_matrix(q_sub23_r(:,:,i),4,8);
     rightImage_synth(:,:,i) = haar_reverse_multilevel(rightImage_dq(:,:,i),2);
 end
-figure; image(uint8(rightImage_synth)), title('Right Image Synth');
-figure; image(uint8(leftImage_synth)), title('Left Image Synth');
+subplot(1,3,1),subimage(uint8(rightImage_synth)), title('Right Image Synth');
+subplot(2,3,2),subimage(uint8(leftImage_synth)), title('Left Image Synth');
 
 % Encode each eye's image using filters of different (usually chromatically opposite) colors, red and blue
 rightImage_synth  = imtranslate(rightImage_synth,[10, 0]);
 
-figure,image(rightImage_synth), title('Anaglyph Synth');
+subplot(3,3,3),subimage(rightImage_synth), title('Anaglyph Synth');
 r = zeros(size(rightImage_synth));
 gb = zeros(size(leftImage_synth));
 r(:,:,1) = double(rightImage_synth(:,:,1));