1 files changed, 0 insertions, 183 deletions

diff --git a/SD-VBS/benchmarks/texture_synthesis/src/matlab/modskew.m b/SD-VBS/benchmarks/texture_synthesis/src/matlab/modskew.m
deleted file mode 100755
index 3de0e3c..0000000
--- a/SD-VBS/benchmarks/texture_synthesis/src/matlab/modskew.m
+++ /dev/null
@@ -1,183 +0,0 @@
-function [chm, snrk] = modskew(ch,sk,p);
-
-% Adjust the sample skewness of a vector/matrix, using gradient projection,
-% without affecting its sample mean and variance.
-%
-% This operation is not an orthogonal projection, but the projection angle is
-% near pi/2 when sk is close to the original skewness, which is a realistic
-% assumption when doing iterative projections in a pyramid, for example
-% (small corrections to the channels' statistics).
-%
-%       [xm, snrk] = modskew(x,sk,p);
-%               sk: new skweness
-%               p [OPTIONAL]:   mixing proportion between sk0 and sk 
-%                               it imposes (1-p)*sk0 + p*sk,
-%                               being sk0 the current skewness.
-%                               DEFAULT: p = 1;
-
-%
-% JPM. 2/98, IODV, CSIC
-% 4/00, CNS, NYU
-
-Warn = 0;  % Set to 1 if you want to see warning messages
-if ~exist('p'), 
-  p = 1;
-end
-
-N=prod(size(ch));       % number of samples
-me=mean2(ch);
-ch=ch-me;
-
-for n=2:6,
-        m(n)=mean2(ch.^n);
-end
-
-sd=sqrt(m(2));  % standard deviation
-s=m(3)/sd^3;    % original skewness
-snrk = snr(sk, sk-s); 
-sk = s*(1-p) + sk*p;
-
-% Define the coefficients of the numerator (A*lam^3+B*lam^2+C*lam+D)
-
-A=m(6)-3*sd*s*m(5)+3*sd^2*(s^2-1)*m(4)+sd^6*(2+3*s^2-s^4);
-B=3*(m(5)-2*sd*s*m(4)+sd^5*s^3);
-C=3*(m(4)-sd^4*(1+s^2));
-D=s*sd^3;
-
-a(7)=A^2;
-a(6)=2*A*B;
-a(5)=B^2+2*A*C;
-a(4)=2*(A*D+B*C);
-a(3)=C^2+2*B*D;
-a(2)=2*C*D;
-a(1)=D^2;
-
-% Define the coefficients of the denominator (A2+B2*lam^2)
-
-A2=sd^2;
-B2=m(4)-(1+s^2)*sd^4;
-
-b=zeros(1,7);
-b(7)=B2^3;
-b(5)=3*A2*B2^2;
-b(3)=3*A2^2*B2;
-b(1)=A2^3;
-
-
-if 0, % test
-
-        lam = -2:0.02:2;
-        S = (A*lam.^3+B*lam.^2+C*lam+D)./...
-                sqrt(b(7)*lam.^6 + b(5)*lam.^4 + b(3)*lam.^2 + b(1));
-%       grd = ch.^2 - m(2) - sd * s * ch;
-%       for lam = -1:0.01:1,
-%               n = lam*100+101;
-%               chp = ch + lam*grd;
-%               S2(n) = mean2(chp.^3)/abs(mean2(chp.^2))^(1.5);
-%       end
-        lam = -2:0.02:2;
-figure(1);plot(lam,S);grid;drawnow
-%       snr(S2, S-S2)
-
-end % test
-
-% Now I compute its derivative with respect to lambda
-
-d(8) = B*b(7);
-d(7) = 2*C*b(7) - A*b(5);
-d(6) = 3*D*b(7);
-d(5) = C*b(5) - 2*A*b(3);
-d(4) = 2*D*b(5) - B*b(3);
-d(3) = -3*A*b(1);
-d(2) = D*b(3) - 2*B*b(1);
-d(1) = -C*b(1);
-
-d = d(8:-1:1);
-mMlambda = roots(d);
-
-tg = imag(mMlambda)./real(mMlambda);
-mMlambda = real(mMlambda(find(abs(tg)<1e-6)));
-lNeg = mMlambda(find(mMlambda<0));
-if length(lNeg)==0,
-        lNeg = -1/eps;
-end
-lPos = mMlambda(find(mMlambda>=0));
-if length(lPos)==0,
-        lPos = 1/eps;
-end
-lmi = max(lNeg);
-lma = min(lPos);
-
-lam = [lmi lma];
-mMnewSt = polyval([A B C D],lam)./(polyval(b(7:-1:1),lam)).^0.5;
-skmin = min(mMnewSt);
-skmax = max(mMnewSt);
-
-
-% Given a desired skewness, solves for lambda
-
-if sk<=skmin & Warn,
-        lam = lmi;
-        warning('Saturating (down) skewness!');
-        skmin
-elseif sk>=skmax & Warn,
-        lam = lma;
-        warning('Saturating (up) skewness!');
-        skmax
-else
-
-
-% The equation is sum(c.*lam.^(0:6))=0
-
-c=a-b*sk^2;
-
-c=c(7:-1:1);
-
-r=roots(c);
-
-% Chose the real solution with minimum absolute value with the rigth sign
-lam=-Inf;
-co=0;
-for n=1:6,
-        tg = imag(r(n))/real(r(n));
-        if (abs(tg)<1e-6)&(sign(real(r(n)))==sign(sk-s)),
-                co=co+1;
-                lam(co)=real(r(n));
-        end
-end
-if min(abs(lam))==Inf & Warn,
-        display('Warning: Skew adjustment skipped!');
-        lam=0;
-end
-
-p=[A B C D];
-
-if length(lam)>1,
-        foo=sign(polyval(p,lam));
-        if any(foo==0),
-                lam = lam(find(foo==0));
-        else
-                lam = lam(find(foo==sign(sk)));         % rejects the symmetric solution
-        end
-        if length(lam)>0,
-                lam=lam(find(abs(lam)==min(abs(lam)))); % the smallest that fix the skew
-                lam=lam(1);
-        else
-                lam = 0;
-        end
-end
-end % if else
-
-% Modify the channel
-chm=ch+lam*(ch.^2-sd^2-sd*s*ch);        % adjust the skewness
-chm=chm*sqrt(m(2)/mean2(chm.^2));               % adjust the variance
-chm=chm+me;                             % adjust the mean
-                                        % (These don't affect the skewness)
-% Check the result
-%mem=mean2(chm);
-%sk2=mean2((chm-mem).^3)/mean2((chm-mem).^2).^(3/2);
-%sk - sk2
-%SNR=snr(sk,sk-sk2)
-
-
-