JOINT SCENE AND SIGNAL MODELING FOR WAVELET-BASED VIDEO CODING WITH CELLULAR NEURAL-NETWORK ARCHITECTURE

This paper presents a joint scene and signal modeling for the design o f 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 neighbo rhood binding with Gibbs random field and the signal modeling characte rized by the matching of the wavelet coefficient distribution. With th is joint modeling, the quantization becomes adaptive to not only wavel et 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 s patial neighborhood constraints. Such spatial constraint allows the qu antization to shift its bit allocation, if necessary, to those percept ually more important coefficients so that the preservation of scene st ructure can be achieved. This joint modeling enables the quantization to reach beyond the limit of the traditional statistical signal modeli ng-based approaches which often lack scene adaptivity. Furthermore, th e dynamically enforced spatial constraints of the Gibbs random field a re able to overcome the shortcomings of the artificial block division which are usually the major source of distortion when the video is cod ed by block-based approaches at low bit rate. In addition, we introduc e a cellular neural network architecture for the hardware implementati on of this proposed adaptive quantization. We prove that this cellular neural network does converge to the desired steady state with the sug gested update scheme. The adaptive quantization scheme based on the jo int scene and signal modeling has been successfully applied to videoco nferencing application and very favorable results have been obtained. We believe that this joint modeling-based video coding will have an im pact on many other applications because it is able to simultaneously p erform signal adaptive and scene adaptive quantization.