Coding using Gaussian mixture and generalized Gaussian models

Abstract

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.