Better Compression Performance by Compressing Sliced Bit Planes of Image Using Weighted Finite Automata

Weighted Finite Automata (WFA) is an efficient structure for digital image storage and is successfully applied for the image compression purpose. This paper introduces the idea of slicing image into eight bit planes and then compressing each bit planes individually using proposed WFA inference algorithm. Furthermore, the proposed algorithm uses the concept of invertible resolution wise mapping in order to create rich families of similarity and thus minimize the WFA size and eventually achieve better compression. The algorithm is simulated successfully for gray scale test images.