Abstract

Reversible Colour Transforms (RCTs) in conjunction with Bi-level Burrows–Wheeler Compression Algorithm (BBWCA) allows for high-level lossless image compression as demonstrated in this study. The RCTs transformation results in exceedingly coordinated image information among the neighbouring pixels as compared to the RGB colour space. This aids the Burrows–Wheeler Transform (BWT) based compression scheme and achieves compression ratios of high degree at the subsequent steps of the program. Validation has been done by comparing the proposed scheme across a range of benchmarks schemes and the performance of the proposed scheme is above par the other schemes. The proposed compression outperforms the techniques exclusively developed for 2-D electrocardiogram (EEG), RASTER map and Color Filter Array (CFA) image compression. The proposed system shows no dependency over parameters like image size, its type or the medium in which it is captured. A comprehensive analysis of the proposed scheme concludes that it achieves a significant increase in compression and depicts comparable complexity similar to the various benchmark schemes.

Highlights

  • Lossless image compression is addressed as one of the most challenging tasks among the research community

  • This research work is oriented around the enhancement of lossless compression of colour images based on the Bi-level Burrows–Wheeler Compression Algorithm (BBWCA) utilizing colour space transformations

  • The BBWCA was tested on sets of colour images to guess the efficiency of the Reversible Colour Transforms (RCTs)

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Summary

Introduction

Lossless image compression is addressed as one of the most challenging tasks among the research community. This research work is oriented around the enhancement of lossless compression of colour images based on the Bi-level Burrows–Wheeler Compression Algorithm (BBWCA) utilizing colour space transformations. Reversible integer colour transform transforms the imaging data into various colour spaces [29,41] and has been utilized for lossless image compression by JPEG 2000 [29,42,43,44] This colour conversion helps in achieving high compression by reducing an image’s entropy prior to encoding [38,39].

Experimental Setup And Results
Lossless Compression Of Colour Images
Lossless Compression of RASTER Maps
Lossless Compression of CFA Images
Lossless Compression of 2-D EEG Data
Scalability to High Resolution Imagery
Findings
Conclusions
Full Text
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