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.
An extension of conventional block motion compensation (BMC), overlapped block motion compensation (OBMC) has been shown to reduce residual errors and blocking effects in motion-compensated video. However, the overlap creates a noncausal spatial dependence between blocks and complicates motion estimation (ME) for OBMC. Iterative methods have traditionally been employed for overlapped block motion estimation (OBME). For compression, the rate for the motion vector field (MVF) may also be constrained. This work considers several rate-constrained OBME algorithms, both iterative and noniterative. Experiments demonstrate that a simple raster-scan algorithm is effective as a suboptimal, noniterative solution, with comparable or better rate-distortion performance and computational complexity than iterative OBME algorithms. Depending on the application, either this method or a simple block-matching algorithm plus iteration are the most attractive of the tested OBME schemes.
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