To use the Bjontegaard metric as a cost function in kmeans clustering, you need to build a distance matrix between all curve pairs using the Bjontegaard metric and then replace the custom distance with this matrix in the "kmedoids" function.
Here is some sample code to help you with the same:
%{
Format your curves in the below format to directly use the code below
curves = {
struct('rate', R1, 'psnr', P1),
struct('rate', R2, 'psnr', P2),
...
struct('rate', RN, 'psnr', PN)
};
%}
N = length(curves);
D = zeros(N); % Distance matrix
for i = 1:N
for j = i+1:N
try
bd = bjontegaard(curves{i}.rate, curves{i}.psnr, curves{j}.rate, curves{j}.psnr, 'rate'); % or 'dsnr'
catch
bd = inf; % in case of interpolation error
end
D(i, j) = abs(bd);
D(j, i) = D(i, j); % symmetric
end
end
k = 3; % Number of clusters
[idx, C] = kmedoids(D, k, 'Distance', 'precomputed');
Note: We are using the "kmedoids" function here since using a custom distance is not supported in the "kmeans" function.
For more information regarding the "kmedoids" function, refer to the following documentation: https://www.mathworks.com/help/stats/kmedoids.html
