Image compression overview

TL;DR

This paper provides an overview of recent image compression techniques, focusing on lossy methods that balance file size reduction with visual quality, highlighting innovations from the last decade.

Contribution

It surveys recent advancements in image compression methods, emphasizing new principles and variations over traditional techniques in the past decade.

Findings

Recent techniques achieve better compression ratios

New principles improve visual quality at lower bitrates

Variations of existing methods dominate recent research

Abstract

Compression plays a significant role in a data storage and a transmission. If we speak about a generall data compression, it has to be a lossless one. It means, we are able to recover the original data 1:1 from the compressed file. Multimedia data (images, video, sound...), are a special case. In this area, we can use something called a lossy compression. Our main goal is not to recover data 1:1, but only keep them visually similar. This article is about an image compression, so we will be interested only in image compression. For a human eye, it is not a huge difference, if we recover RGB color with values [150,140,138] instead of original [151,140,137]. The magnitude of a difference determines the loss rate of the compression. The bigger difference usually means a smaller file, but also worse image quality and noticable differences from the original image. We want to cover compression techniques mainly from the last decade. Many of them are variations of existing ones, only some of them uses new principes.