can anyone please convert this codef or 512*512*3 lena image, it is working well with * lena image

          close all; 
          clear all;
          clc;

          *[A,map]=imread('lena_2.bmp');
           RGB = ind2rgb(A,map);
          figure(1);         
           imshow(RGB);
          title('original image');
          pause(2);*
           [Height,Width,Depth] = size(RGB);
          if Depth > 1
          A1 = double(RGB(:,:,1));
          else
          A1 = double(RGB);
          end
          Qtype = 'uniform'; 
          B = 5; 
          y=DPCM_2D(A1,Qtype,B);
          %-------------function DPCM_2D-----------
          function y=DPCM_2D(f,Qtype,B,map)

          L = 2^B; % # levels in the quantizer%2^5=32
          [Height,Width,Depth] = size(f);% get the image 'lena256.bmp'%512*512*1
          %
          % compute optimal predictor coefficients
          [alfa,E] = LinearPredict_2D(f,map);%LinearPredict_2D('lena256.bmp');
          %
          % Design the uniform quantizer using 5*std dev as the limits
          switch Qtype
          case 'uniform'
          dMin = mean2(E) - 5*std2(E);
          dMax = mean2(E) + 5*std2(E);
          q = 2*dMax/L; % step size
          q2 = q/2;
          dR = linspace(dMin,dMax,L+1); % decision intervals
          rL = zeros(L,1); % reconstruction levels
          for k = 1:L
          rL(k) = dR(k)+q2;
          end
          case 'nonuniform'
          [DR,C] = dpcmQuantizer(E,B);% design a B-bit
          end
          Mu = mean2(f);% mean value of the image
          f = f - Mu;% remove mean value
          % Implement the 2D DPCM
          y = zeros(Height,Width);% array to store reconstructed image
          pe = zeros(Height,Width);% array to store differential image
          peq = zeros(Height,Width);% array to store quantizeddifferential image
          x1 = zeros(Height+1,Width+2); % array to store reconstructed image
          y(1,:) = f(1,:) + Mu;
          y(:,1) = f(:,1) + Mu;
          %
          f = padarray(f,[1 2 ],'symmetric','pre');
          % First row, first column no prediction
          x1(1,:) = f(1,:);% store previously reconstructed pixels
          x1(:,1) = f(:,1);
          for r = 2:Height
          for c = 2:Width
          xhat = alfa(1)*x1(r,c-1) + alfa(2)*x1(r-1,c) + ...
          alfa(3)*x1(r-1,c-1)+ alfa(4)*x1(r-1,c+1);
          pe(r,c) = f(r,c) - xhat;
          switch Qtype
          case 'uniform'
          for k = 1:L
          if pe(r,c)>dR(k) && pe(r,c)<=dR(k+1)
          peq(r,c) = rL(k);
          elseif pe(r,c)<= dR(1)
          peq(r,c) = rL(1);
          elseif pe(r,c) > dR(L+1)
          peq(r,c) = rL(L);
          end
          end
          case 'nonuniform'
          %{
          for k = 1:L
          if (pe(r,c)>DR(k) && pe(r,c)<=DR(k+1))
          peq(r,c) = C(k);
          end
          end
          %}
          d1 = abs(pe(r,c)-C(1));
          for k = 2:L
          d2 = abs(pe(r,c)-C(k));
          if d2<d1
          d1 = d2;
          J = k;
          end
          end
          peq(r,c) = C(J);
          end
          x1(r,c) = peq(r,c) + xhat;% previously
          y(r,c) = x1(r,c) + Mu;% mean added reconstructed pixel
          %y.Height;
          end
          end
          % Display differential and reconstructed images
          figure(4),imshow(pe,[]), title('Differential image');
          pause(2);
          figure(5);

          imshow(y,[]);
          title('Compressed using DPCM');
          end
      %%%%function LinearPredict
      function [alfa,pe] = LinearPredict_2D(I,map)%I='lena256.bmp'%512*512 double

      % [alfa,pe] = LinearPredict 2D(I)
      % I = input intensity image
      % alfa = predictor coefficient vector
      % pe = prediction error image of the same size as I
      % Uses 2D linear prediction to
      % create the differential image
      % xhat(m,n) = alfa1(1)*x(m,n-1)+alfa(2)*x(m-1,n)+...
      % alfa(3)*x(m-1,n-1)+alfa(4)*x(m-1,n+1)
      [Height,Width,Depth] = size(I);
      if Depth > 1
      I1 = double(I(:,:,1));
      else
      I1 = double(I);%512*512 double
      end
      AAA=ind2rgb(I1,map);
      Mu = mean2(I);% mean value of the image%0.4866
      I = I - Mu;% mean removed input image
      r00 = mean2(I .* I);
      r01 = sum(sum(I(:,2:Width).* I(:,1:Width-1)))/(Height*(Width-1));
      r10 = sum(sum(I(2:Height,:).* I(1:Height-1,:)))/((Height-1)*Width);
      r11 = sum(sum(I(2:Height,2:Width).* I(1:Height-1,1:Width-1)))/((Height-1)*(Width-1));
      r12 = sum(sum(I(2:Height,3:Width).* I(1:Height-1,1:Width-2)))/((Height-1)*(Width-2));
      r02 = sum(sum(I(1:Height,3:Width).* I(1:Height,1:Width-2)))/(Height*(Width-2));
      alfa = inv([r00 r11 r10 r12; r11 r00 r01 r01;r10 r01 r00 r02; r12 r01 r02 r00])/[r01 r10 r11 r11];
      % Implement the Linear Predictor
      pe = zeros(Height,Width,Depth);% array to store prediction errors
      %I1 = padarray(I1,[1 1],’symmetric’,’pre’);
      I = padarray(I,[1 2],'symmetric','pre');
      for r = 2:Height+1
      for c = 2:Width+1
      xhat = alfa(1)*I(r,c-1)+alfa(2)*I(r-1,c)+ ...
      alfa(3)*I(r-1,c-1) + alfa(4)*I(r-1,c+1);
      pe(r-1,c-1) = I(r,c) - xhat;% differential pixel
      end
      end

code in bold is changes that I made to convert indexed image to rgb but than also getting the image I attached

[Image Analyst]

I have no idea what you're doing, or what you want to do, what a "codef" is, and what you want to convert the codef into. Try http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer this.

And I'm not sure what the point of this code is:

[A,map]=imread('lena_2.bmp');
RGB = ind2rgb(A,map);

What is the class of A? Is it a 2D indexed image to start with?

[tina jain]

yes it is a 2D indexed image. I am trying to apply 2D DPCM on the image