ANALISA METODE SHANNON ENTROPY DAN DIFFERENTIAL EVOLUTION UNTUK KOMPRESI GAMBAR

Abstract

One of the most important phases in image storage is compression. Most current imagecompression methods are spatial. In this article, we present an image compression techniquebased on Multi-level Thresholding. The grayscale images are divided into groups based on thenet probabilistic division. To determine the grouping uncertainty, Shannon Entropy is used.Optimization methods have also been added to obtain more optimal settings. DifferentialEvolution is an optimization technique. Image histogram is a graph that depicts the distributionof pixel intensity values of an image. Image compression performance measurement wasmeasured using FSIM (Feature Similarity Index Measure) and SSIM (Structural Similarity IndexMeasure).