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The IUP Journal of Telecommunications

Various Image Compression Techniques: A Review

Article Details

Pub. Date	: February' 2022
Product Name	: The IUP Journal of Telecommunications
Product Type	: Article
Product Code	: IJTC020222
Author Name	: Garima Garg* and Raman Kumar**
Availability	: YES
Subject/Domain	: Arts & Humanities
Download Format	: PDF Format
No. of Pages	: 11

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Abstract

The use of images in a wide variety of applications has expanded rapidly due to technological developments that have influenced the operations and availability of advanced image modification management software. Despite technological breakthroughs in storage and transmission, demand for storage capacity and communication bandwidth exceeds available capacity. As a result, image compression has proven to be a helpful technique. When it comes to image compression, we do not just focus on lowering size but also focus on not sacrificing image quality or information. This review outlines the primary image compression algorithms, both lossy and lossless, their benefits, drawbacks, and research opportunities. The examination of several compression techniques aids in the identification of advantageous qualities and selection of proper compression method. Some general criteria for choosing the optimum compression algorithm for an image have been suggested based on the review.

Introduction

An image is a two-dimensional communication processed by the human visual system. The impulses that depict images are usually analog. Computer applications convert them from analog to digital for processing, storage and transmission (Sundhu and Rajkamal, 2009). A digital image is a 2D pixel array. Image compression reduces the amount of storage space required for photos and movies, thus improving storage and transmission performance. Lossy or lossless image compression is possible. Lossless compression entails compressing data so that it may be decompressed into an identical reproduction of the original (Arora and Shukla, 2014; Pancholi et al., 2014; and Singh et al., 2016). However, in lossy compression techniques, some of the image's finer details can be sacrificed in order to save a little more bandwidth or storage space.