Lossy to Lossless Spatially Scalable Depth Map Coding with Cellular Automata

Spatially scalable image coding algorithms are mostly based on linear filtering techniques that give a multi-resolution representation of the data. Reversible cellular automata can be instead used as simpler, non-linear filter banks that give similar performance. In this paper, we investigate the use of reversible cellular automata for lossy to lossless and spatially scalable coding of smooth multi-level images, such as depth maps. In a few cases, the compression performance of the proposed coding method is comparable to that of the JBIG standard, but, under most test conditions, we show better compression performances than those obtained with the JBIG or the JPEG2000 standards. The results stimulate further investigation into cellular automata-based methods for multi-level image compression.