Lossy-to-lossless progressive coding of depth-map images using competing constant and planar models

In this paper we propose an extension of our lossy-to-lossless progressive coding method by placing the planar model in a competition with the piecewise constant model during the region reconstruction stage of the algorithm. A sequence of lossy images is generated using an hierarchical segmentation, of the initial image, based on region merging. The progressive coding method is able to compress this sequence of images by encoding the elements that represent the differences between two consecutive images. The method is splitting some regions from the current image segmentation using an encoded set of contours, and it is defining a set of new regions, which are reconstructed using either the piecewise constant model or the planar model. An efficient solution is proposed for encoding the model parameters in a progressive way. Results show an improvement of 3 - 4 dB compared to the baseline method based only on constant regions, and for a wide range it achieves almost similar results with the non-progressive methods.