Bit rate reduction techniques for absolute moment block truncation coding

Abstract

Block Truncation Coding uses a two-level moment preserving quantizer that adapts to local properties of the images. It has the features of low computation load and low memory requirement while its bit rate is only 2.0 bits per pixel. A more efficient algorithm, the absolute moment BTC (AMBTC) has been extensively used in the field of signal compression because of its simple computation and better MSE performance. We propose postprocessing methods to further reduce the entropy of two output data of AMBTC, including the bit map and two quantization data (a, b). A block of a 2×4 bit map is packaged into a byte-oriented symbol. The entropy can be reduced from 0.965 bpp to 0.917 bpp on average for our test images. The two subimages of quantization data (a, b) are postprocessed by the Peano Scan. This postprocess can further reduce differential entropy about 0.4 bit for a 4×4 block. By applying arithmetic coding, the total bit reduction is about 0.3~0.4 bpp. The bit rate can reach 1.6~1.7 bpp with the same quality as traditional AMBTC.