A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage

Asif Rajput, Jianqiang Li, Faheem Akhtar, Zahid Hussain Khand, Jason C Hung, Yan Pei and Anko Börner

International Journal of Distributed Sensor Networks, 2022, vol. 18, issue 3, 15501329221083168

Abstract: The idea of applying integer Reversible Colour Transform to increase compression ratios in lossless image compression is a well-established and widely used practice. Although various colour transformations have been introduced and investigated in the past two decades, the process of determining the best colour scheme in a reasonable time remains an open challenge. For instance, the overhead time (i.e. to determine a suitable colour transformation) of the traditional colour selector mechanism can take up to 50% of the actual compression time. To avoid such high overhead, usually, one pre-specified transformation is applied regardless of the nature of the image and/or correlation of the colour components. We propose a robust selection mechanism capable of reducing the overhead time to 20% of the actual compression time. It is postulated that implementing the proposed selection mechanism within the actual compression scheme such as JPEG-LS can further reduce the overhead time to 10%. In addition, the proposed scheme can also be extended to facilitate network-based compression–decompression mechanism over distributed systems.

Keywords: Reversible colour transformation; lossless image processing; image coding; awareness computing; image compression (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:18:y:2022:i:3:p:15501329221083168

DOI: 10.1177/15501329221083168

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