Joint image classification and compression using hierarchical table-lookup vector quantization
Navin Chaddha, K.O. Perlmutter, Robert M. Gray · 2002
Classification and compression play important roles today in communicating digital information and their combination is useful in many applications. The aim is to produce image classification without any further signal processing on the compressed image. This paper presents techniques for the design of block based joint classifier and quantizer classifiers/encoders implemented by table lookups. In the table lookup classifiers/encoders, input vectors to the encoders are used directly as addresses in code tables to choose the codewords with the appropriate classification information. In order to preserve manageable table sizes for large dimension VQs, hierarchical structures that quantize the vector successively in stages are used. Since both the classifier/encoder and decoder are implemented by table lookups, there are no arithmetic computations required in the final system implementation. They are unique in that both the classifier/encoder and the decoder are implemented with only table lookups and are amenable to efficient software and hardware solutions.