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An Image Knowledge Based Video Codec For Low Bitrates.

The goal of image coding is to reduce the number of bits required to represent an image or a sequence of images. For the new generation of video coders the coding should be tailored to image understanding and should more and more replace the purely statistical or waveform coding techniques. At present the more conventional algorithms are prefered in the light of a direct hardware realization. Most of these methods are block based and do not consider the global image features. In this paper an attempt is made to extend a hybrid DPCM/transform coding configuration with object based features. For videoconferencing and videophone applications we intend to use a simple method exploiting the knowlegde of typical interpersonal videocommunication scenes such as head and shoulders. The first step is the segmentation of the image into participants and background. For this segmentation information available in the coder is combined with knowledge obtained from the actual analysis. To make the segmentation more robust to various input signals we accept the fact that objects often are more easily recognized in images that have a very low spatial sampling rate. At this low level details are not taken into account which yields a more homogenous segmentation. This leads to a pyramidal image data approach. By applying a coarse to fine structure the procedure starts at a low resolution level and is refined at ever-increasing resolutions. A comparison is given of a coder with and without this a priori knowlegde.