Lossless image compression: application of Bi-level Burrows Wheeler Compression Algorithm (BBWCA) to 2-D data

This research paper demonstrates the robustness of Bi-level Burrows Wheeler Compression Algorithm (BBWCA) in terms of the compression efficiency for different types of image data. The scheme was designed to take advantage of the increased inter-pixel redundancies resulting from a two pass Burrows Wheeler Transformation (BWT) stage and the use of Reversible Colour Transform (RCT). In this research work, BBWCA was evaluated for raster map images, Colour Filter Array (CFA) images as well as 2-D ElectroEncephaloGraphy (EEG) data and compared against benchmark schemes. Validation has been carried out on various examples and they show that BBWCA is capable of compressing 2-D data effectively. The proposed method achieves marked improvement over the existing methods in terms of compression size. BBWCA is 18.8 % better at compressing images as compared to High Efficiency Video Codec (HEVC) and 21.2 % more effective than LZ4X compressor for CFA images. For the EEG data, BBWCA is 17 % better at compressing images as compared to WINRK and 25.2 % more effective than NANOZIP compressor. However, for the Raster images PAQ8 supersedes BBWCA by 11 %. Among the different schemes compared, the proposed scheme achieves overall best performance and is well suited to small and large size image data compression. The parallelization process reduces the execution time particularly for large size images. The parallelized BBWCA scheme reduces the execution time by 31.92 % on average as compared to the non-parallelized BBWCA.