KeCoDe_encoder_color

PURPOSE ^

KeCoDe_ENCODER_COLOR applies DCT-based image coding algorithms to the

SYNOPSIS ^

function [Results] = KeCoDe_encoder_color(Im,algorit,directorio,ficherin,entropias,N_it)

DESCRIPTION ^

 KeCoDe_ENCODER_COLOR applies DCT-based image coding algorithms to the
 input 256*256*3 RGB color image to obtain a file with the desired
 entropy (in bits/pixel).

 The program stores the encoded file in the folder\name provided by the user.
 Particular appendices to the given file name will be added depending on the
 selected algorithm (see names below).

 The encoded file can be decoded with KeCoDe_DECODER_COLOR.M
 Besides, the encoder routine also gives the decoded image, the entropy
 of the code (in bits/pix) and a number of distortion measures.

  * MODEL 1: JPEG91.   Wallace, Comm. of the ACM, Vol.34(4):30-44, (1991)
             block DCT transform coding with quantization based on JPEG-like
             linear CSF vision model in each chromatic channel (YUV).
             It does not include masking relations at all.
             The name of the encoded file will be: name_jpeg91_c_entropy_1.zip

             Note, though, that this is not exactly the JPEG standard since
             (1) 16*16 DCT blocks are used, (2) run length encoding, DPCM DC encoding
             and block information arrangement are done in a non-standard way, and (3)
             the final entropy coding is done by the Matlab zip routine.
             Nevertheless this implementation is the appropriate for fair
             comparison with the other algorithm since the above (non-fundamental)
             details are implemented in the same way.

  * MODEL 2: Gutierrez11. Gutierrez et al., Recent Patents in Signal Proc., In press (2011)
             block DCT transform coding plus chromatic divisive normalization in each
             YUV channel and constant insensitivity SVM coefficient selection.
             Dedicated SVMs are trained in a vision model domain that takes into
             account both frequency seletivity and masking relations among coefficients.
             The name of the encoded file will be:  name_gutierrez09_c_entropy_1.zip

 The bit rate of the above algorithms is controlled by different
 parameters:

   * Algorithm 1 depend on the Control Parameter, 'CP'.
     Smaller CP values imply more coarse quantization thus giving smaller
     files and more distorted images.

   * Algorithm 2 depend on two parameters:
     (1) the insensitivity parameter of the SVM, 'Epsilon'.
     (2) the number of bits used to encode the SVM weights, 'Bits'.
     For a fixed number of bits, smaller Epsilon values imply keeping more
     support vectors (or useful coefficients) and hence larger files and better
     quality images.

 The user has to provide a target entropy value. The program then
 sets the values of the control parameters (CP, or Epsilon and Bits) and
 iteratively modifies them to achieve the target entropy for the particular image.

 SYNTAX:
 [Results] = KeCoDe_encoder_color(Im,MODEL,'output_folder','name',target_entropies,Num_iterat)

 Input variables:
 ----------------
  * Im             :  256*256*3 image matrix double precision numbers in the range [0 255]
  * MODEL          :  1-2
  * 'output_folder':  String with the folder where it will be written the output file(s)
  * 'name'         :  String with the name of the output file
                      Note that an appendix to this name will be added
                      depending on the coding algorithm.
  * target_entropies: Vector containing the set of target entropies
                      (an image can be compressed at different entropies
                      with a single call to this function)
                      There will be as many output files as target entropy
                      values.
  * Num_iterat     :  Number of iterations to look for the target entropy

 Output:
 -------
  * Results        :  Struct variable with the following fields

       - Results(i).Image   = Decoded image corresponding to the i-th value
                              of the target entropy vector.
       - Results(i).Entropy = Entropy (in bits/pix): file_size/256^2
       - Results(i).RMSE    = RMSE distortion of the i-th decoded image
       - Results(i).SSIM    = Structural SIMilarity Index of the i-th
                              decoded image.
                              (See Wang et al. IEEE Tr. Im. Proc., 2004
                              for a description of this distortion measure)
       - Results(i).SCIELab_exp1= Spatial CIE Lab distortion with summation exponent
                              equal to 1, default in S-CIELab computations.
                              (See Zhang & Wandell, Sig. Proc. 1998
                              for a description of this distortion measure)
       - Results(i).SCIELab_exp2= Spatial CIE Lab distortion with summation exponent
                              equal to 2. (See Zhang & Wandell, Sig. Proc. 1998
                              for a description of this distortion
                              measure)

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 
0002 %
0003 % KeCoDe_ENCODER_COLOR applies DCT-based image coding algorithms to the
0004 % input 256*256*3 RGB color image to obtain a file with the desired
0005 % entropy (in bits/pixel).
0006 %
0007 % The program stores the encoded file in the folder\name provided by the user.
0008 % Particular appendices to the given file name will be added depending on the
0009 % selected algorithm (see names below).
0010 %
0011 % The encoded file can be decoded with KeCoDe_DECODER_COLOR.M
0012 % Besides, the encoder routine also gives the decoded image, the entropy
0013 % of the code (in bits/pix) and a number of distortion measures.
0014 %
0015 %  * MODEL 1: JPEG91.   Wallace, Comm. of the ACM, Vol.34(4):30-44, (1991)
0016 %             block DCT transform coding with quantization based on JPEG-like
0017 %             linear CSF vision model in each chromatic channel (YUV).
0018 %             It does not include masking relations at all.
0019 %             The name of the encoded file will be: name_jpeg91_c_entropy_1.zip
0020 %
0021 %             Note, though, that this is not exactly the JPEG standard since
0022 %             (1) 16*16 DCT blocks are used, (2) run length encoding, DPCM DC encoding
0023 %             and block information arrangement are done in a non-standard way, and (3)
0024 %             the final entropy coding is done by the Matlab zip routine.
0025 %             Nevertheless this implementation is the appropriate for fair
0026 %             comparison with the other algorithm since the above (non-fundamental)
0027 %             details are implemented in the same way.
0028 %
0029 %  * MODEL 2: Gutierrez11. Gutierrez et al., Recent Patents in Signal Proc., In press (2011)
0030 %             block DCT transform coding plus chromatic divisive normalization in each
0031 %             YUV channel and constant insensitivity SVM coefficient selection.
0032 %             Dedicated SVMs are trained in a vision model domain that takes into
0033 %             account both frequency seletivity and masking relations among coefficients.
0034 %             The name of the encoded file will be:  name_gutierrez09_c_entropy_1.zip
0035 %
0036 % The bit rate of the above algorithms is controlled by different
0037 % parameters:
0038 %
0039 %   * Algorithm 1 depend on the Control Parameter, 'CP'.
0040 %     Smaller CP values imply more coarse quantization thus giving smaller
0041 %     files and more distorted images.
0042 %
0043 %   * Algorithm 2 depend on two parameters:
0044 %     (1) the insensitivity parameter of the SVM, 'Epsilon'.
0045 %     (2) the number of bits used to encode the SVM weights, 'Bits'.
0046 %     For a fixed number of bits, smaller Epsilon values imply keeping more
0047 %     support vectors (or useful coefficients) and hence larger files and better
0048 %     quality images.
0049 %
0050 % The user has to provide a target entropy value. The program then
0051 % sets the values of the control parameters (CP, or Epsilon and Bits) and
0052 % iteratively modifies them to achieve the target entropy for the particular image.
0053 %
0054 % SYNTAX:
0055 % [Results] = KeCoDe_encoder_color(Im,MODEL,'output_folder','name',target_entropies,Num_iterat)
0056 %
0057 % Input variables:
0058 % ----------------
0059 %  * Im             :  256*256*3 image matrix double precision numbers in the range [0 255]
0060 %  * MODEL          :  1-2
0061 %  * 'output_folder':  String with the folder where it will be written the output file(s)
0062 %  * 'name'         :  String with the name of the output file
0063 %                      Note that an appendix to this name will be added
0064 %                      depending on the coding algorithm.
0065 %  * target_entropies: Vector containing the set of target entropies
0066 %                      (an image can be compressed at different entropies
0067 %                      with a single call to this function)
0068 %                      There will be as many output files as target entropy
0069 %                      values.
0070 %  * Num_iterat     :  Number of iterations to look for the target entropy
0071 %
0072 % Output:
0073 % -------
0074 %  * Results        :  Struct variable with the following fields
0075 %
0076 %       - Results(i).Image   = Decoded image corresponding to the i-th value
0077 %                              of the target entropy vector.
0078 %       - Results(i).Entropy = Entropy (in bits/pix): file_size/256^2
0079 %       - Results(i).RMSE    = RMSE distortion of the i-th decoded image
0080 %       - Results(i).SSIM    = Structural SIMilarity Index of the i-th
0081 %                              decoded image.
0082 %                              (See Wang et al. IEEE Tr. Im. Proc., 2004
0083 %                              for a description of this distortion measure)
0084 %       - Results(i).SCIELab_exp1= Spatial CIE Lab distortion with summation exponent
0085 %                              equal to 1, default in S-CIELab computations.
0086 %                              (See Zhang & Wandell, Sig. Proc. 1998
0087 %                              for a description of this distortion measure)
0088 %       - Results(i).SCIELab_exp2= Spatial CIE Lab distortion with summation exponent
0089 %                              equal to 2. (See Zhang & Wandell, Sig. Proc. 1998
0090 %                              for a description of this distortion
0091 %                              measure)
0092 %
0093 function [Results] = KeCoDe_encoder_color(Im,algorit,directorio,ficherin,entropias,N_it)
0094 warning('off','MATLAB:dispatcher:InexactMatch')
0095 Im=double(Im);
0096 if algorit==1
0097 
0098     Desired_entropy=[0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 1   1.2 1.4 1.6  1.8 2   2.2];
0099     Approximate_uCP=[1.3 3.1 4   4.7 5.3 5.7 6.2 6.5 7.2 7.7 8.2 8.7  9.1 9.5 9.8];
0100     for i=1:length(entropias)
0101         if entropias(i)>max(Desired_entropy),
0102            parametro(i)=10.5;
0103         elseif entropias(i)<min(Desired_entropy),
0104            parametro(i)=1.1;
0105         else
0106            parametro(i)=interp1(Desired_entropy,Approximate_uCP,entropias(i));
0107         end
0108     end
0109 elseif algorit==2
0110  Desired_entropy=[0.1  0.2  0.3  0.4   0.5   0.6   0.7   0.8    1      1.2    1.4    1.6     1.8     2       2.2];
0111  Approx_Epsilon= [1.6  0.23 0.06 0.02  0.006 0.004 0.002 0.0016 0.0009 0.0005 0.0003 0.00016 0.00017 0.00012 0.0001];
0112  Approx_bits   = [2.5  2.5  2.5  2.5   2.5   2.5   3     3.5    4.5    5      6      6.5     7       7.5     8];
0113     for i=1:length(entropias)
0114         if entropias(i)>max(Desired_entropy),
0115            parametro(i)=0.00005;
0116            bits_in(i)=8;
0117         elseif entropias(i)<min(Desired_entropy),
0118            parametro(i)=1.9;
0119            bits_in(i)=2;
0120         else
0121            parametro(i)=interp1(Desired_entropy,Approx_Epsilon,entropias(i));
0122            bits_in(i)=interp1(Desired_entropy,Approx_bits,entropias(i));
0123         end
0124     end
0125 else
0126    disp('Not a valid algorithm selection')
0127    algorit=1000;
0128 end
0129 if algorit<=2
0130     normaliz=1;
0131     To=[100 0 0];
0132     dUmax=40;
0133     dVmax=43;
0134     niveles=1;
0135     if algorit==1
0136         Results=entropy_jpeg(Im,normaliz,To,dUmax,dVmax,[parametro' parametro' parametro'],entropias,directorio,ficherin,N_it,niveles);
0137     else
0138         K=kernel2d_variance(16,1);
0139         exponente=[2 2 2];
0140         tipo_alfa=1;
0141         Results=entropy_nl_svr(Im,normaliz,To,dUmax,dVmax,[parametro' parametro' parametro'],bits_in,K,exponente,tipo_alfa,entropias,directorio,ficherin,N_it);
0142     end
0143 end
0144

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