matlab实现jpeg算法进行图像压缩的源代码(共10页).docx

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1、精选优质文档-倾情为你奉上function jpeg % THIS WORK IS SUBMITTED BY:% OHAD GAL%close all; % =% section 1.2 + 1.3% =% the following use of the function: % plot_bases( base_size,resolution,plot_type )% will plot the 64 wanted bases. I will use zero-padding for increased resolution% NOTE THAT THESE ARE THE SAME BAS

2、ES !% for reference I plot the following 3 graphs:% a) 3D plot with basic resolution (64 plots of 8x8 pixels) using surf function% b) 3D plot with x20 resolution (64 plots of 160x160 pixels) using mesh function% c) 2D plot with x10 resolution (64 plots of 80x80 pixels) using mesh function% d) 2D plo

3、t with x10 resolution (64 plots of 80x80 pixels) using imshow function% NOTE: matrix size of pictures (b),(c) and (d), can support higher frequency = higher bases% but I am not asked to draw these (higher bases) in this section !% the zero padding is used ONLY for resolution increase !% get all base

4、 pictures (3D surface figure)plot_bases( 8,1,surf3d ); % get all base pictures (3D surface figure), x20 resolutionplot_bases( 8,20,mesh3d ); % get all base pictures (2D mesh figure), x10 resolutionplot_bases( 8,10,mesh2d ); % get all base pictures (2D mesh figure), x10 resolutionplot_bases( 8,10,gra

5、y2d ); % =% section 1.4 + 1.5% =% for each picture 0.9 perform a 2 dimensional dct on 8x8 blocks.% save the dct inside a cell of the size: 10 cells of 128x128 matrix% show for each picture, its dct 8x8 block transform. for idx = 0:9 % load a picture switch idx case 0,1, input_image_128x128 = im2doub

6、le( imread( sprintf( %d.tif,idx ),tiff ) ); otherwise, input_image_128x128 = im2double( imread( sprintf( %d.tif,idx),jpeg ) ); end % perform DCT in 2 dimension over blocks of 8x8 in the given picture dct_8x8_image_of_128x128idx+1 = image_8x8_block_dct( input_image_128x128 ); if (mod(idx,2)=0) figure

7、; end subplot(2,2,mod(idx,2)*2+1); imshow(input_image_128x128); title( sprintf(image #%d,idx) ); subplot(2,2,mod(idx,2)*2+2); imshow(dct_8x8_image_of_128x128idx+1); title( sprintf(8x8 DCT of image #%d,idx) );end % =% section 1.6% =% do statistics on the cell array of the dct transforms% create a mat

8、rix of 8x8 that will describe the value of each dct-base % over the transform of the 10 given pictures. since some of the values are% negative, and we are interested in the energy of the coefficients, we will% add the abs()2 values into the matrix.% this is consistent with the definition of the Pars

9、eval relation in Fourier Coefficients % initialize the average matrix mean_matrix_8x8 = zeros( 8,8 ); % loop over all the picturesfor idx = 1:10 % in each picture loop over 8x8 elements (128x128 = 256 * 8x8 elements) for m = 0:15 for n = 0:15 mean_matrix_8x8 = mean_matrix_8x8 + . abs( dct_8x8_image_

10、of_128x128idx(m*8+1:8,n*8+1:8) ).2; end endend % transpose the matrix since the order of the matrix is elements along the columns,% while in the subplot function the order is of elements along the rowsmean_matrix_8x8_transposed = mean_matrix_8x8; % make the mean matrix (8x8) into a vector (64x1)mean

11、_vector = mean_matrix_8x8_transposed(:); % sort the vector (from small to big)sorted_mean_vector,original_indices = sort( mean_vector ); % reverse order (from big to small)sorted_mean_vector = sorted_mean_vector(end:-1:1);original_indices = original_indices(end:-1:1); % plot the corresponding matrix

12、 as asked in section 1.6figure;for idx = 1:64 subplot(8,8,original_indices(idx); axis off; h = text(0,0,sprintf(%4d,idx); set(h,FontWeight,bold); text(0,0,sprintf( n_%1.1fdb,20*log10(sorted_mean_vector(idx) );end % add a title to the figuresubplot(8,8,4);h = title( Power of DCT coefficients (section

13、 1.6) );set( h,FontWeight,bold ); % =% section 1.8% =% picture 8 is chosen% In this section I will calculate the SNR of a compressed image againts% the level of compression. the SNR calculation is defined in the header % of the function: which is given below.% if we decide to take 10 coefficients wi

14、th the most energy, we will add% zeros to the other coefficients and remain with a vector 64 elements long% (or a matrix of 8x8) % load the original imageoriginal_image = im2double( imread( 8.tif,jpeg ) ); % I will use this matrix to choose only the wanted number of coefficients% the matrix is initi

15、alized to zeros - dont choose any coefficient at allcoef_selection_matrix = zeros(8,8); % compressed picture set (to show the degrading)compressed_set = 1 3 5 10 15 20 30 40; % this loop will choose each time, the next-most-energetic coefficient, % to be added to the compressed image - and thus to i

16、mproove the SNRfor number_of_coefficient = 1:64 % find the most energetic coefficient from the mean_matrix y,x = find(mean_matrix_8x8=max(max(mean_matrix_8x8); % select if for the compressed image coef_selection_matrix(y,x) = 1; % replicate the selection matrix for all the parts of the dct transform

17、 % (remember that the DCT transform creates a set of 8x8 matrices, where % in each matrix I need to choose the coefficients defined by the % matrix ) selection_matrix = repmat( coef_selection_matrix,16,16 ); % set it as zero in the mean_matrix, so that in the next loop, we will % choose the next-mos

18、t-energetic coefficient mean_matrix_8x8(y,x) = 0; % choose the most energetic coefficients from the original image % (total of coefficients for this run in the loop) compressed_image = image_8x8_block_dct(original_image) .* selection_matrix; % restore the compressed image from the given set of coefi

19、cients restored_image = image_8x8_block_inv_dct( compressed_image ); % calculate the snr of this image (based on the original image) SNR(number_of_coefficient) = calc_snr( original_image,restored_image ); if isempty(find(number_of_coefficient=compressed_set) if (number_of_coefficient=1) figure; subp

20、lot(3,3,1); imshow( original_image ); title( original image ); end subplot(3,3,find(number_of_coefficient=compressed_set)+1); imshow( restored_image ); title( sprintf(restored image with %d coeffs,number_of_coefficient) ); endend % plot the SNR graphfigure;plot( 1:64,20*log10(SNR) );xlabel( numer of

21、 coefficients taken for compression );ylabel( SNR db ( 20*log10(.) ) );title( SNR graph for picture number 8, section 1.8 );grid on; % -% I N N E R F U N C T I O N I M P L E M E N T A T I O N% -% % -% pdip_dct2 - implementation of a 2 Dimensional DCT% assumption: input matrix is a square matrix !% -

22、function out = pdip_dct2( in ) % get input matrix sizeN = size(in,1); % build the matrixn = 0:N-1;for k = 0:N-1 if (k0) C(k+1,n+1) = cos(pi*(2*n+1)*k/2/N)/sqrt(N)*sqrt(2); else C(k+1,n+1) = cos(pi*(2*n+1)*k/2/N)/sqrt(N); end end out = C*in*(C); % -% pdip_inv_dct2 - implementation of an inverse 2 Dim

23、ensional DCT% assumption: input matrix is a square matrix !% -function out = pdip_inv_dct2( in ) % get input matrix sizeN = size(in,1); % build the matrixn = 0:N-1;for k = 0:N-1 if (k0) C(k+1,n+1) = cos(pi*(2*n+1)*k/2/N)/sqrt(N)*sqrt(2); else C(k+1,n+1) = cos(pi*(2*n+1)*k/2/N)/sqrt(N); end end out =

24、 (C)*in*C; % -% plot_bases - use the inverse DCT in 2 dimensions to plot the base pictures% Note: we can get resolution be zero pading of the input matrix !% that is by calling: in = zeros(base_size*resolution)% where: resolution is an integer 1% So I will use zero pading for resolution (same as in

25、the fourier theory)% instead of linear interpolation.% -function plot_bases( base_size,resolution,plot_type ) figure;for k = 1:base_size for l = 1:base_size in = zeros(base_size*resolution); in(k,l) = 1; % ask for the base-harmonic (k,l) subplot( base_size,base_size,(k-1)*base_size+l ); switch lower

26、(plot_type) case surf3d, surf( pdip_inv_dct2( in ) ); case mesh3d, mesh( pdip_inv_dct2( in ) ); case mesh2d, mesh( pdip_inv_dct2( in ) ); view(0,90); case gray2d, imshow( 256*pdip_inv_dct2( in ) ); end axis off; endend % add a title to the figuresubplot(base_size,base_size,round(base_size/2);h = tit

27、le( Bases of the DCT transform (section 1.3) );set( h,FontWeight,bold ); % -% image_8x8_block_dct - perform a block DCT for an image% -function transform_image = image_8x8_block_dct( input_image ) transform_image = zeros( size( input_image,1 ),size( input_image,2 ) );for m = 0:15 for n = 0:15 transf

28、orm_image( m*8+1:8,n*8+1:8 ) = . pdip_dct2( input_image( m*8+1:8,n*8+1:8 ) ); endend % -% image_8x8_block_inv_dct - perform a block inverse DCT for an image% -function restored_image = image_8x8_block_inv_dct( transform_image ) restored_image = zeros( size( transform_image,1 ),size( transform_image,

29、2 ) );for m = 0:15 for n = 0:15 restored_image( m*8+1:8,n*8+1:8 ) = . pdip_inv_dct2( transform_image( m*8+1:8,n*8+1:8 ) ); endend % -% calc_snr - calculates the snr of a figure being compressed% assumption: SNR calculation is done in the following manner:% the deviation from the original image is co

30、nsidered % to be the noise therefore:% noise = original_image - compressed_image% the SNR is defined as: % SNR = energy_of_image/energy_of_noise% which yields: % SNR = energy_of_image/(original_image-compressed_image)2)% -function SNR = calc_snr( original_image,noisy_image ) original_image_energy = sum( original_image(:).2 );noise_energy = sum( (original_image(:)-noisy_image(:).2 );SNR = original_image_energy/noise_energy;以下是1-9号原图像，放到matlab的.m文件目录里，重命名9个图像名为1、2、3、4、5、6、7、8、9专心-专注-专业