ANALISA METODE SHANNON ENTROPY DAN DIFFERENTIAL EVOLUTION UNTUK KOMPRESI GAMBAR

Abstract

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