Least mean square error-based image compression using block truncation coding

Abstract

Block truncation coding is one of the image compression techniques which has low computational complexity. This lossy compression technique divides the image into equal block sizes and the pixels in each block are replaced with a high mean and low mean value based on the mean as the threshold. The bit plane thus obtained after quantisation is transmitted along with the mean and variance. This gives a bit rate of 2 bits per pixel. Nevertheless, when the block size is increased for obtaining higher compression ratio, the image is dominated by severe blocking artefacts and blurred edges thereby reducing the visual quality of the image. In the proposed method the mean square error is calculated iteratively for the blocks. The pixels are grouped into higher order and lower order blocks and the group which produces the least mean square error is transmitted along with the mean values of the higher order and lower order block. This implies that the image which is retrieved in the receiver gives the optimised visual quality in the least mean square sense.