Analysis of trellis quantization for near-lossless image coding
Summary form only given. We discuss several variations to the original algorithm proposed by Ke and Marcellin (see Proc. IEEE ICIP, Washington DC, 1995). We have extended the trellis quantization (TQ) scheme by performing two-row joint optmizations instead of optimizing row by row. Unfortunately, while increasing the computation time quite a bit, this has lead only to marginal coding gains. A progressive probability update scheme has lead to much better convergence and to a 0.3 bpp gain over the original fixed scheme. When using lossy plus near-lossless coding the lossy version can be used for better context modelling without increasing the computational complexity of the near-lossless residual coding. Improvements of 0.1-0.2 bpp were observed. Since it is computationally infeasible to include more pixels to be determined by the TQ process, one has the choice of either using better prediction/context-modelling or doing TQ. Our tests indicate that the preference should be given to sophisticated prediction/modelling.