Image Compression Using Improved Method

Abstract

Effective use of digital images requires certain techniques to lower the number of bits needed to represent them. The goal of compression is to reduce the size of an image while maintaining its information and originality. This research aims to achieve this target by developing a fuzzy logic and histogram-based image compression type detection approach. The main goal is to provide an approach that employs fuzzy logic and graphs for detecting the type of image compression. In order to decide the compression technique depending on the number of color levels in the image which was recovered with the use of histogram, the suggested technique combines a new combination of fuzzy logic as well as image histogram analysis. The experimental findings demonstrated the suggested technique's robustness and efficiency in detecting the best approach for each type of image compression. After 75 images with varying color densities were evaluated, the system's compression rate was approximately 95%. In addition to being a significant contribution to image processing, the study finds practical applications in the following areas: remote sensing through satellite; facsimile transmission of medical images in computer tomography magnetic resonance imaging; teleconferencing systems; military communication systems via radars; geological surveys; and communications systems built by computers.