Lossless image compression using kernel based Global Structure Transform (GST)

Lossless data compression using the variants of Burrows-Wheeler Transform (BWT) with various compression encoders has proven its effectiveness. This research provides a unique method for lossless compression of color images by improving the Global Structure Transform (GST) stage of the Burrows-Wheeler Compression Algorithm (BWCA). The proposed model applies the Move-To-Front (MTF) transform at the GST stage by selecting 2-D block (kernel) of BWT data. This method has resulted in a high occurrence of same gray levels in the kernel. Moreover, the symbol map for the MTF Encoder is generated only for the available gray levels in the kernel. The overall redundancy of the MTF indexes increases at the GST stage of the BWCA which results in increased compression.