Multum in parvo: Toward a generic compression method for binary images.

Data compression is an active field of research as the requirements to efficiently store and retrieve data at minimum time and cost persist to date. Lossless or lossy compression of bi-level data, such as binary images, has an equally crucial factor of importance. In this work, we explore a generic, application-independent method for lossless binary image compression. The first component of the proposed algorithm is a predetermined fixed-size codebook comprising 8 x 8-bit blocks of binary images along with the corresponding codes of shorter lengths. The two variations of the codebook--Huffman codes and Arithmetic codes--have yielded considerable compression ratios for various binary images. In order to attain higher compression, we introduce a second component--the row-column reduction coding--which removes additional redundancy. The proposed method is tested on two major areas involving bi-level data. The first area of application consists of binary images. Empirical results suggest that our algorithm outperforms the standard JBIG2 by at least 5% on average. The second area involves images consisting of a predetermined number of discrete colors, such as digital maps and graphs. By separating such images into binary layers, we employed our algorithm and attained efficient compression down to 0.035 bits per pixel. --P.ii.