Only K Means clustering algorithm is applied to compress Image to decrease its size but I want to apply other algorithms.
Maybe you should try 'Expectation Maximization'. If you used Kmeans already this would be simple to implement. Instead of looking at the euclidean distance when checking to which cluster a certain point belongs in the next iteration, you now look at the distances relative to the standard deviation. So if a point is closer to the center of let's say Cluster A, considering the euclidean distance, it might be closer to the Cluster B relative to the standard deviation. In this case "closer" means having a color that is similar to the one of Cluster B. The described distance which considers the standard deviations is the so called Mahalanobis distance.
you can also use Self-Organizing Maps for image compression. there are many literatures which use SOMs successfully in compressing images such as the following:
Hierarchical and dynamic SOM applied to image compression
https://ieeexplore.ieee.org/document/1223472/
A novel Kohonen SOM-based image compression architecture suitable for moderate density FPGAs
Article A novel Kohonen SOM-based image compression architecture sui...
and many more.
Best regards
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