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小木头5898

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专业: 信号理论与信号处理

[交流] 【求助】JPEG的Matlab实现

下面是我自己实现的JPEG程序　量化后编码是用的RLC　没有使用Huffman（没发现合适的）　但是过程中JPEG之后的图像有很明显的块效应　重建之后PSNR不理想
而且没有实现码率bpp的可控制　
敬请各位虫友指导　讨论
如果有更好的JPEG实现程序　希望能贴出来或发给小弟　yan_li19850430@yahoo.com

急盼回复

////////////////////////////////////////////////////////////
% My_JPEG main function
function My_JPEG

clear all;
clc

%load image
I_Ori=imread('lena.bmp');
I=double(I_Ori);
figure(1)
imshow(I_Ori)

% DCT_8*8 Transform  and  Quantification
%dct_transformed_image = Classic_DCT( I );
dct_transformed_image = image_8x8_block_flowgraph_forward_dct( I );
figure(2)
II=uint8(dct_transformed_image);
imshow(II)

Q_8x8=[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];
quantization_matrix_128x128 = repmat(Q_8x8,64,64); %for coarse quantization
%quantization_matrix_128x128 = repmat((ceil(double(Q_8x8)./40)),64,64 ); %for fine quantization

quantized_image_128x128 =  round(dct_transformed_image ./quantization_matrix_128x128); %round operation should be done here for lossy quantization

figure(3)
imshow(uint8(quantized_image_128x128))

% Entropy Coding
%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;

% 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=1:4096 %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_Single_Column_Image(Temp_Vector_Index, block_index);  %select 1 block sequentially from the ZigZaged_Single_Column_Image
      end
      non_zero_value_index_array = find(single_block_image_vector_64~=0); % index array of next non-zero entry in a block
      number_of_non_zero_entries = length(non_zero_value_index_array);  % # of non-zero entries in a block

% Case 1: if first ac coefficient has no leading zeros then encode first coefficient
      if non_zero_value_index_array(1)==1,
         run=0; % no leading zero
         run_level_pairs=cat(1,run_level_pairs, run, single_block_image_vector_64(non_zero_value_index_array(1)));
      end

% Case 2: loop through each non-zero entry
      for n=2:number_of_non_zero_entries,
         % check # of leading zeros (run)
         run=non_zero_value_index_array(n)-non_zero_value_index_array(n-1)-1;
         run_level_pairs=cat(1, run_level_pairs, run, single_block_image_vector_64(non_zero_value_index_array(n)));
      end

% Case 3: "End of Block" mark insertion
      run_level_pairs=cat(1, run_level_pairs, 255, 255);
end
  Compressed_image_size = size(run_level_pairs);       % file size after compression
  Compression_Ratio = 262144/Compressed_image_size(1,1);
  Rate=8/ Compression_Ratio

% Entropy Decoding
% % %  -------------------------------------------------------------------
% % %  -------------------------------------------------------------------
% % %             DECODING
% % %  -------------------------------------------------------------------
% % %  -------------------------------------------------------------------

%---------------------- Run Level Decoding ---------------------------
% construct  ZigZaged_Single_Column_Image from Run Level Pair
c=[];
for n=1:2:size(run_level_pairs), % loop through run_level_pairs
      % Case 1 & Cae 2
      % concatenate zeros according to 'run' value
      if run_level_pairs(n)<255 % only end of block should have 255 value
         zero_count=0;
         zero_count=run_level_pairs(n);
         for l=1:zero_count % concatenation of zeros accouring to zero_count
            c=cat(1,c,0); % single zero concatenation
         end
         c=cat(1,c,run_level_pairs(n+1)); % concatenate single'level' i.e., a non zero value

      % Case 3: End of Block decoding
      else
         number_of_trailing_zeros= 64-mod(size(c),64);
         for l= 1:number_of_trailing_zeros % concatenate as much zeros as needed to fill a block
            c=cat(1,c,0);
         end
      end
end
%---------------------------------------------------------------------

%---------------------------------------------------------------------
% prepare the ZigZaged_Single_Column_Image vector (each column represents 1 block) from the
% intermediate concatenated vector "c"
for i=1:4096
      for j=1:64
         ZigZaged_Single_Column_Image(j,i)=c(64*(i-1)+j);
      end
end
%---------------------------------------------------------------------


%--------------------------- reverse zigzag --------------------------
%reverse zigzag procedure using the matrix indexing capability of MatLab (specially the ':' operator)
Single_column_quantized_image = ZigZaged_Single_Column_Image(reverse_zigzag_order_8x8,

;
%---------------------------------------------------------------------

%image matrix construction from image column
quantized_image_128x128 = col2im(Single_column_quantized_image, [8 8], [512 512], 'distinct');

% Inverse_DCT_8*8 Transform and Inverse Quantification

dct_transformed_image_IQ = quantized_image_128x128 .*quantization_matrix_128x128;

%restored_image = image_8x8_block_inv_dct(dct_transformed_image_IQ );
restored_image = image_8x8_block_flowgraph_inverse_dct( dct_transformed_image_IQ );

III=uint8(restored_image);
figure(4)
imshow(III)
% Reconstruct image

%SNR
PSNR=psnr(restored_image, I);

end

[ Last edited by gjliu on 2009-5-11 at 13:29 ]

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