TABLE OF CONTENTS Effective quantizers and source codes are based on effective source models. In statistically-based image coding, classification is a method for identifying portions of the image data that have similar statistical properties so that the data can be encoded using quantizers and codes matched to the data statistics. Classification in this context is, thus, a method for identifying a set of source models appropriate for an image, and then using the respective model parameters to define the quantizers and codes used to encode the image.
This chapter describes an image coding algorithm based on block classification and trellis coded quantization. The basic principles of block classification for coding are developed and several classification methods are outlined. Trellis coded quantization is described, and a formulation presented that is appropriate for arithmetic coding of the TCQ codeword indices. Finally, the performance of the classification-based subband image coding algorithm is presented.
Figure 1 shows the frequency partition induced by a 22-band decomposition. This decomposition can be thought of as a 2-level uniform split (16-band uniform) followed by a 2-level octave split of the lowest frequency subband. The 2-level octave split exploits the correlation present in the lowest frequency subband [36]. Typically the histogram of a subband is peaked around zero. One possible approach to model the subbands is to treat them as memoryless generalized Gaussian (GG) sources [24]. Several researchers [15], [35], [36]
