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Adaptive quantization with spatial constraints in subband video compression using wavelets

Coding of the high frequency subbands has been recognized as the key to the success of subband coding. However, the existing schemes are not very efficient in exploiting the spatial and spectral localization properties resulting from wavelet-based subband decomposition. We present a novel adaptive quantization scheme with spatial constraints. This scheme is capable of exploiting simultaneously both the spectrally and spatially localized characteristics of the high frequency subbands. A multi-modal Laplacian distribution is used to model the spectral distribution of a high frequency subband and a Gibbs random field is employed to model the spatial constraints. The modeling is incorporated into a non-iterative MAP estimation to yield the quantized subbands. This quantization scheme reduces significantly the activities in the high frequency bands while preserving the perceptually important structures. Such an adaptive quantization makes the entire subband video compression scheme amenable to low bit rate coding.