Joint Scene and Signal Modeling for Wavelet-Based VideoCoding with Cellular Neural Network Architecture

Abstract

This paper presents a joint scene and signal modeling for the design of an adaptive quantization scheme applied to the wavelet coefficients in subband video coding applications. The joint modeling includes two integrated components: the scene modeling characterized by the neighborhood binding with Gibbs random field and the signal modeling characterized by the matching of the wavelet coefficient distribution. With this joint modeling, the quantization becomes adaptive to not only wavelet coefficient signal distribution but also the prominent image scene structures. The proposed quantization scheme based on the joint scene and signal modeling is accomplished through adaptive clustering with spatial neighborhood constraints. Such spatial constraint allows the quantization to shift its bit allocation, if necessary, to those perceptually more important coefficients so that the preservation of scene structure can be achieved. This joint modeling enables the quantization to reach beyond the limit of the traditional statistical signal modeling-based approaches which often lack scene adaptivity. Furthermore, the dynamically enforced spatial constraints of the Gibbs random field are able to overcome the shortcomings of the artificial block division which are usually the major source of distortion when the video is coded by block-based approaches at low bit rate. In addition, we introduce a cellular neural network architecture for the hardware implementation of this proposed adaptive quantization. We prove that this cellular neural network does converge to the desired steady state with the suggested update scheme. The adaptive quantization scheme based on the joint scene and signal modeling has been successfully applied to videoconferencing application and very favorable results have been obtained. We believe that this joint modeling-based video coding will have an impact on many other applications because it is able to simultaneously perform signal adaptive and scene adaptive quantization.