Classification techniques for digital map compression

TLDR: A comparison is made of the performance of various image classification techniques as applied to color cartographic maps, with a clear lead over the K-means clustering algorithm and vector quantization scheme.

Abstract: A comparison is made of the performance of various image classification techniques as applied to color cartographic maps is compared. The maps have a lot of graininess due to imperfections in the printing process, which decreases the efficiency of compression techniques. The color maps are classified using the K-means clustering algorithm and vector quantization (VQ), with neighborhood classification to improve the visual quality and compression ratio. The classification is performed in various image representation schemes. The performance of the classifier is evaluated on the basis of the visual quality of the classified image, the time required to classify the image, and compression achieved on the classified image. In terms of computation times, K-means exhibits a clear lead over the VQ classification scheme. However, the VQ classifier converges in fewer iterations than the K-means algorithm. The algorithms eliminated almost all misclassified pixels that were present in the image. The K-means algorithm with neighborhood classification, however, resulted in the filling in of one of the letters and a deterioration in the quality of the lines. >