code-jpeg.rar_JPEG图像压缩

function jpeg % This is a JPEG encoding & decoding program of still image. % it does not use level shifting. % Discrete Cosine transform (DCT) is performed both by classical & Chen's % Flowgraph methods. Predefined JPEG quantization array & Zigzag order are % used here. 'RUN', 'LEVEL' coding is used instead of Huffman coding. % Compression ratio is compared for each DCT method. Effect of coarse and fine quantization is % also examined. The execution time of 2 DCT methods is also checked. % In addition, most energatic DCT coefficients are also applied to examine % the effect in MatLab 7.4.0 R2007a. Input is 9 gray scale pictures & % output is 9*9=81 pictures to compare. Blocking effect is obvious. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %-------------------------- Initialization ----------------------------- % JPEG default quantization array Q_8x8 =uint8([ 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 62 18 22 37 56 68 109 103 77 24 35 55 64 81 104 113 92 49 64 78 87 103 121 120 101 72 92 95 98 112 100 103 99]); % I am interested in the energy of the dct coefficients, I will use % this matrix (found from long observation) to select dct coefficients. % lowest number -> highest priority dct_coefficient_priority_8x8 =[ 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]; % if we decide to take 10 coefficients with the most energy, we will assign % 99 to ignore the other coefficients and remain with a matrix of 8x8 %This suitable Zigzag order is formed from the JPEG standard ZigZag_Order = uint8([ 1 9 2 3 10 17 25 18 11 4 5 12 19 26 33 41 34 27 20 13 6 7 14 21 28 35 42 49 57 50 43 36 29 22 15 8 16 23 30 37 44 51 58 59 52 45 38 31 24 32 39 46 53 60 61 54 47 40 48 55 62 63 56 64]); % Finding the reverse zigzag order (8x8 matrix) reverse_zigzag_order_8x8 = zeros(8,8); for k = 1:(size(ZigZag_Order,1) *size(ZigZag_Order,2)) reverse_zigzag_order_8x8(k) = find(ZigZag_Order== k); end; Compressed_image_size=0; %--------------------------------------------------------------------- close all; for Image_Index = 8:8 % the whole program will be tested for 9 images (0->8) figure; %keep the input-output for each image seperately %--------------------------load a picture ---------------------------- switch Image_Index case {0,1}, input_image_128x128 = im2double( imread( sprintf( '%d.tif',Image_Index ),'tiff' ) ); otherwise, input_image_128x128 = im2double( imread( sprintf( '%d.tif',Image_Index),'jpeg' ) ); end %-------------------------- ------------------------------------------ %---------------- show the input image ------------------------------- subplot(3,3,1); imshow(input_image_128x128); title( sprintf('original image #%d',Image_Index) ); %--------------------------------------------------------------------- for Quantization_Quality = 0:1 % 0 -> coarse quantization, 1 -> fine quantization for DCT_type = 0:1 % 0 -> classic DCT, 1 -> Flowgraph fast DCT for chosen_number_of_dct_coefficient = 1:63:64 % select 1 or 64 dct coefficients %---------------- choose energetic DCT coefficients ------------------ % I will use this matrix to choose only the wanted number of dct coefficients % the matrix is initialized to zeros -> zero coefficient is chosen at the beginning coef_selection_matrix_8x8 = zeros(8,8); % this loop will choose 1 dct coefficients each time for l=1:chosen_number_of_dct_coefficient % find the most energetic coefficient from the mean_matrix [y,x] = find(dct_coefficient_priority_8x8==min(min(dct_coefficient_priority_8x8))); % select specific coefficients by location index y,x for the image to be compressed coef_selection_matrix_8x8(y,x) = 1; % set it as 99 for the chosen dct coefficient, so that in the next loop, we will choose the "next-most-energetic" coefficient dct_coefficient_priority_8x8(y,x) = 99; end % replicate the selection matrix for all the parts of the dct transform selection_matrix_128x128 = repmat( coef_selection_matrix_8x8,16,16 ); %--------------------------------------------------------------------- tic ; % start mark for elapsed time for encoding & decoding %------------------------- Forward DCT ------------------------------- % for each picture perform a 2 dimensional dct on 8x8 blocks. if DCT_type==0 dct_transformed_image = Classic_DCT(input_image_128x128) .* selection_matrix_128x128; else dct_transformed_image = image_8x8_block_flowgraph_forward_dct(input_image_128x128) .* selection_matrix_128x128; end %--------------------------------------------------------------------- %---------------- show the DCT of image ------------------------------ % one can use this portion to show DCT coefficients of the image % subplot(2,2,2); % imshow(dct_transformed_image); % title( sprintf('8x8 DCT of image #%d',Image_Index) ); %--------------------------------------------------------------------- %normalize dct_transformed_image by the maximum coefficient value in dct_transformed_image Maximum_Value_of_dct_coeffieient = max(max(dct_transformed_image)); dct_transformed_image = dct_transformed_image./Maximum_Value_of_dct_coeffieient; %integer conversion of dct_transformed_image dct_transformed_image_int = im2uint8( dct_transformed_image ); %-------------------- Quantization ----------------------------------- % replicate the 'Q_8x8' for at a time whole (128x128) image quantization if Quantization_Quality==0 quantization_matrix_128x128 = repmat(Q_8x8,16,16 ); %for coarse quantization else quantization_matrix_128x128 = repmat(uint8(ceil(double(Q_8x8)./40)),16,16 ); %for fine quantization end %at a time whole image (128x128) quantization quantized_image_128x128 = round(dct_transformed_image_int ./quantization_matrix_128x128) ; %round operation should be done here for lossy quantization %--------------------------------------------------------------------- % Break 8x8 block into columns Single_column_quantized_image=im2col(quantized_image_128x128, [8 8],'distinct'); %--------------------------- zigzag ---------------------------------- % using the MatLab Matrix indexing power (specially the ':' operator) rather than any function ZigZaged_Single_Column_Image=Single_column_quantized_image(ZigZag_Order,:); %--------------------------------------------------------------------- %---------------------- Run Level Coding ----------------------------- % construct Run Level Pair from ZigZaged_Single_Column_Image run_level_pairs=uint8([]); for block_index=1:256 %block by block - total 256 blocks (8x8) in the 128x128 image single_block_image_vector_64(1:64)=0; for Temp_Vector_Index=1:64 single_block_image_vector_64(Temp_Vector_Index) = ZigZaged