On Optimal Bit Allocation for Classification-Based Source-Dependent Transform Coding

An optimal bit allocation is presented for classification-based source-dependent transform coding. A vector of transform coefficients is considered to have been produced by a mixture of processes. The available bit resource is distributed optimally in two stages: (1) bit allocation is performed for each class of coefficient vectors, and (2) bit allocation is performed for each vector coefficient. The solution for low bit rates imposing nonnegative bit resource is also presented. The rate-distortion bound of the classification-based source coding is derived

Coding Using Gaussian Mixture And Generalized Gaussian Models

In transform image coding, the histograms of transform coefficients can be approximately modeled by generalized Gaussian (GG) random variables. However, the GG models may not fit the DC distribution. One approach uses DPCM for the DC data, which greatly complicates bit allocation; another assumes a single Gaussian (SG) model, which may be a poor model. As an alternative, this paper proposes a finite Gaussian mixture (GM) model for the DC data. The GM approach does not require tweaking of the DPCM quantizer stepsize and can allocate bits optimally between the DC and AC data; it is also more flexible than the SG model. Experimentally, the GM method matched DPCM at medium rates and gave 1--5 dB higher PSNR at low and high rates. The GM method also matched the performance of the SG model and gave 0.5--2 dB higher PSNR when the SG assumption failed. 1. INTRODUCTION In block transform coding, an image is divided into a rectangular lattice of N \Theta N blocks, and a transform of each block ..