Comparison Of K-Means And K-Means Plus Plus For Image Compression With Thermographic Images

Compression algorithms have been implemented for k-means and k-means++ clustering and applied to thermographic images. The algorithms have four stages and differ only in the first stage, which is the initialization of the centroids. The compression ratio and quality are primarily dependent on the number of clusters selected for the algorithm. A MATLAB GUI was developed to run the algorithms and a comparison has been performed with subjective evaluations and objective RMS error, peak SNR and compression ratio metrics. The average compression ratio was 1.3 and 1.6 for the k-means and k-means++ clustering respectively. The k-means++ clustering provides subjectively better visual results than the standard k-means clustering.