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 applica tions in image processing, The challenge as well as the opportunity in wavelet-based compression is to exploit the characteristics of the su bband coefficients with respect to both spectral and spatial localitie s, A common problem with many existing quantization methods is that th e inherent image structures are severely distorted with coarse quantiz ation, Observation shows that subband coefficients with the same magni tude generally do not have the same perceptual importance; this depend s on whether or not they belong to clustered scene structures, We prop ose in this paper a novel scene adaptive and signal adaptive quantizat ion scheme capable of exploiting both the spectral and spatial localiz ation properties resulting from wavelet transform, The proposed quanti zation is implemented as a maximum a posteriori probability (MAP) esti mation-based clustering process in which subband coefficients are quan tized to their cluster means, subject to local spatial constraints, Th e intensity distribution of each cluster within a subband is modeled b y an optimal Laplacian source to achieve the signal adaptivity, while spatial constraints are enforced by appropriate Gibbs random fields (G RF) to achieve the scene adaptivity, Consequently, with spatially isol ated coefficients removed and clustered coefficients retained at the s ame time, the available bits are allocated to visually important scene structures so that the information loss is least perceptible. Further more, the reconstruction noise in the decompressed image can be suppre ssed using another GRF-based enhancement algorithm, Experimental resul ts have shown the potentials of this quantization scheme for low bit-r ate image and video compression.