Analysis of Video Content in Multi Codec Formats with Compacted Dither Coding

The digital video content analysis and retrieval is a hot research topic in this era as videos act as an information source. However the content analysis and retrieval is not as simple as meaningful retrieval of information in a normal data system. The visual information of video data lies in high chromatic depth and density. At the same time these data uses different codecs for storage and interpretation. The differences of video codec coding system properties cause for chromatic data differences of video files. This paper elicits the difficulties of dealing with multi codec level video visual classification and it proposes an approach to handle visual features and higher level color attributes of video data even though it is in multi-codecs nature. This approach uses novel compacted dither pattern code extractor for visual classification. This extractor reduces the complexity of colour visual feature extraction and classification. Further, this approach has been tested with various lossy and loss-less video formats to generate a probabilistic coding mechanism. The results of this approach show that it can be further enhanced to calibrate a video visual classification knowledge base.