Classification of compound images based on transform coefficient likelihood

Applications like distance learning and teleconferencing often require compression of images that contain both text and graphics. Because text and graphics have different properties, a compression scheme can benefit by treating the textual and graphical portions of such compound images separately. In this paper, we propose new methods, called transform coefficient likelihood (TCL) schemes, for separating the textual and graphical portions of a compound image. TCL schemes examine the DCT coefficient values of an 8×8 block. For each coefficient, they refer to stored histograms that give the likelihood that a certain value occurs in a text block, or in a graphics block. They then examine the differences in these two likelihoods over all the coefficients in the block to decide whether it contains text or graphics. Experimental results show that the best TCL methods significantly outperform previously proposed techniques