Swarm and Evolutionary Algorithms in Image Compression by F-Transform

This article investigates the application of swarm and evolutionary algorithms, namely SOMA, DE, GA, for optimizing F-transform-based image compression. To do this, we introduce cost function, evaluating approximation decompressed to original image, concerning parameters that control quality F-transform. function is then minimized by selected algorithms find optimal settings compression decompression. We design experiments compare performance F-transform method methods optimized GA on a dataset 10 pictures. In all considered cases, results obtained with completely surpass those one. also apply statistical test (called Wilcoxon signed-rank test) ranking in this issue. The show SOMA DE perform well cases where compressed sizes are small. However, algorithm shows outperformance comparison others more complicated size bigger. terms decompression computation time. Finally, provide visual between tested 128×128 picture. latter much sharper detailed than former.