A scene adaptive and signal adaptive quantization for subband image and video compression using wavelets

Discrete wavelet transform (DWT) provides an advantageous framework of multiresolution space-frequency representation with promising applications in image processing, The challenge as well as the opportunity in wavelet-based compression is to exploit the characteristics of the subband coefficients with respect to both spectral and spatial localities, A common problem with many existing quantization methods is that the inherent image structures are severely distorted with coarse quantization, Observation shows that subband coefficients with the same magnitude generally do not have the same perceptual importance; this depends on whether or not they belong to clustered scene structures, We propose in this paper a novel scene adaptive and signal adaptive quantization scheme capable of exploiting both the spectral and spatial localization properties resulting from wavelet transform, The proposed quantization is implemented as a maximum a posteriori probability (MAP) estimation-based clustering process in which subband coefficients are quantized to their cluster means, subject to local spatial constraints, The intensity distribution of each cluster within a subband is modeled by an optimal Laplacian source to achieve the signal adaptivity, while spatial constraints are enforced by appropriate Gibbs random fields (GRF) to achieve the scene adaptivity, Consequently, with spatially isolated coefficients removed and clustered coefficients retained at the same time, the available bits are allocated to visually important scene structures so that the information loss is least perceptible. Furthermore, the reconstruction noise in the decompressed image can be suppressed using another GRF-based enhancement algorithm, Experimental results have shown the potentials of this quantization scheme for low bit-rate image and video compression.