Abstract

A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area.

Highlights

  • A huge amount of data is produced daily, especially in medical centres and on social media

  • We provide a detailed analysis of the state-of-the-art lossless still image compression techniques

  • The key feature of this paper is that we carry out a comparison based on compression ratio (CR), and explore the effectiveness of each algorithm in terms of compressing an image based on several metrics

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Summary

Introduction

A huge amount of data is produced daily, especially in medical centres and on social media. More than 2.5 quintillion bytes of data are produced daily, and this figure is growing, according to the sixth edition of a report by DOMO [1]. A visual representation of an object is called an image, and a digital image can be defined as a two-dimensional matrix of discrete values. The quantised values of a continuous tone image at discrete locations are called the grey levels or the intensity [18], and the pixel brightness of a digital image is indicated by its corresponding grey level. A greyscale image is a matrix of A × B pixels, and 8-bit and 16-bit greyscale images contain 28 = 256 and 216 = 65,536 different colours, respectively, where the ranges of colour values are from 0–255 and 0–65,535.

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