Classification using vector quantization

The authors describe a simple technique for combining vector quantization and low level classification of images. The goal is to classify automatically certain simple features in an image as part of the compression process to enhance their appearance in the reconstructed image. Images in the training sequence are divided into blocks and each block is classified into a particular class by a human observer. This knowledge is used when designing the code-book so that both small average distortion and accurate implicit classification are achieved. The codebook can also be designed to have different average distortions for the different classes. The technique is a variation on a variable rate tree-structured vector quantizer which is grown by splitting a single terminal node at each iteration. The splitting criterion selection allows tradeoffs among compression rate, distortion, and misclassification rate