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Lossless Image Compression Techniques: A State-of-the-Art Survey

School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan
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Symmetry 2019, 11(10), 1274; https://doi.org/10.3390/sym11101274
Received: 3 September 2019 / Revised: 3 October 2019 / Accepted: 7 October 2019 / Published: 11 October 2019
Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques. View Full-Text
Keywords: lossless and lossy compression; run-length; Shannon–Fano; Huffman; LZW; arithmetic coding; average code length; compression ratio; PSNR and efficiency lossless and lossy compression; run-length; Shannon–Fano; Huffman; LZW; arithmetic coding; average code length; compression ratio; PSNR and efficiency
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Rahman, M.A.; Hamada, M. Lossless Image Compression Techniques: A State-of-the-Art Survey. Symmetry 2019, 11, 1274.

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