Comparison of K-means and K-means++ for image compression with thermographic images

Hridoy Biswas; Scott E. Umbaugh; Dominic Marino; Joseph Sackman

doi:10.1117/12.2587264

12 April 2021 Comparison of K-means and K-means++ for image compression with thermographic images

Hridoy Biswas, Scott E. Umbaugh, Dominic Marino, Joseph Sackman

Proceedings Volume 11743, Thermosense: Thermal Infrared Applications XLIII; 117430U (2021) https://doi.org/10.1117/12.2587264
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Compression algorithms have been implemented for k-means and k-means++ clustering and applied to thermographic images. The overall algorithm has four stages and are the same for the two algorithms except for the initialization of the centroids. The compression ratio and quality are primarily dependent on the number of clusters used 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.

Conference Presentation

Citation Download Citation

Hridoy Biswas, Scott E. Umbaugh, Dominic Marino, and Joseph Sackman "Comparison of K-means and K-means++ for image compression with thermographic images", Proc. SPIE 11743, Thermosense: Thermal Infrared Applications XLIII, 117430U (12 April 2021); https://doi.org/10.1117/12.2587264

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