Lossless Compression of Color Map Images by Context Tree Modeling

Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploits interlayer dependencies. Even though the use of a binary alphabet simplifies the context tree construction and exploits spatial dependencies efficiently, it is expected that an equivalent or better result would be obtained by operating directly on the color image without layer separation. In this paper, we extend the previous context-tree-based method to operate on color values instead of binary layers. We first generate an n-ary context tree by constructing a complete tree up to a predefined depth, and then prune out nodes that do not provide compression improvements. Experiments show that the proposed method outperforms existing methods for a large set of different color map images