Scan predictive vector quantization of multispectral images

Conventional vector quantization (VQ)-based techniques partition an image into nonoverlapping blocks that are then raster scanned and quantized. Image blocks that contain an edge result in high-frequency vectors. The coarse representation of such vectors leads to visually annoying degradations in the reconstructed image. The authors present a solution to the edge-degradation problem based on some earlier work on scan models. The approach reduces the number of vectors with abrupt intensity variations by using an appropriate scan to partition an image into vectors. They show how their techniques can be used to enhance the performance of VQ of multispectral data sets. Comparisons with standard techniques are presented and shown to give substantial improvements.