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40 pages, 2683 KB  
Review
The Impact of State-of-the-Art Techniques for Lossless Still Image Compression
by Md. Atiqur Rahman, Mohamed Hamada and Jungpil Shin
Electronics 2021, 10(3), 360; https://doi.org/10.3390/electronics10030360 - 2 Feb 2021
Cited by 33 | Viewed by 8689
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Advances in Multimedia Signal Processing and Communications)
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