Image Coding based Orthogonal Polynomials Multiresolution Analysis with Joint Probability Context Modeling and Modified Golomb-Rice Entropy Coding

Abstract

The work proposes, a JPEG2000 like compression technique which is based on multiresolution analysis of orthogonal polynomials transformation (OPT) coefficients has been presented with bit modeling for Golomb-Rice entropy coding. Initially, the image under analysis is divided into blocks and OPT is applied to each divided blocks. Then, transformed coefficients are represented as sub bands like structure (multiresolution) and scalar quantization is carried out to the transformed coefficients to reduce the precision. The quantized coefficients are then bit modelled in the bit plane using a joint probability statistical model, and significant bits in the bit plane are chosen. On the selected relevant bits, a geometrically distributed set of context is modelled for further encoding with modified Golomb-Rice encoding to provide compressed data. The decompression procedure is just the reverse of compression procedure. Experiments and analysis are carried out to demonstrate the efficiency of the proposed compression scheme in terms of compression ratio and Peak-Signal-to Noise Ratio (PSNR), and the results are encouraging