A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage. (March 2022)

• Record Type: Journal Article
• Authors: Rajput, Asif; Li, Jianqiang; Akhtar, Faheem; Hussain Khand, Zahid; Hung, Jason C; Pei, Yan; Börner, Anko
• Is Part Of: International journal of distributed sensor networks. Volume 18:Number 3(2022)
• Publication Date: 2022-03
• Subjects: Reversible colour transformation -- lossless image processing -- image coding -- awareness computing -- image compression; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501329221083168
• Languages: English
• ISSNs: 1550-1329

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.