Reduction of syntactic video data clustering complexity in processing with compacted dither coding

The growing consumption of the digital video information is significant in this era. The digital video analysis and retrieval is not as simple as analysis and retrieval of information in normal data system. The visual information of video data lies in very complex nature with its high chromatic depth and density. The extraction of visual features from noisy and complex video data has a hierarchy of different sub systems from video file to chromatic attributes. This paper introduces a novel approach to reduce the video visual feature analyzing complexity and the higher level colour complexity of video data. It comes with simple vector quantization mechanism, high rate performance approach for classification of digital video visual features. Further this approach has tested with various video formats to generate probabilistic coding mechanism. The results of this approach show that it can be further enhanced with video graphical knowledge to guide the visual feature clustering with trained knowledge base.