This article presents a new lossless compression algorithm for computer animation image sequences. The algorithm uses transformation information available in the animation script and floating point depth and object number information at each pixel to perform highly accurate motion prediction with vary low computation. The geometric data (i.e., the depth and object number) can either be computed during the original rendering process and stored with the image or computed on the fly during compression and decompression. In the former case the stored geometric data are very efficientlycomporessed using motion prediction and a new technique called direction coding, typically to 1 to 2 bits per pixel. The geometric data are also useful in z-buffer image compsiting and this new compression algorthm offers a very low storage overhead method for saving the information needed for this comoositing. The overall compression ratio of the new algorithm, including the geometic data overhead, in compared to conventional spatial linear prediction compression and block-matching motion. The algorithm improves on a previous motion prediction algorithm by incorporating block predictor switching and color ratio predition. The combination of thes techniques gives compression ratios 30% better than those reported previously.

This paper presents a new lossless compression algorithm for computer animation image sequences.The algorithm uses transformation information available in the animation script and floating point depth and object number information stored at each pixel to perform highly accurate motion prediction with very low computation.The geometric data, i.e., the depth and object number, is very efficiently compressed using motion prediction and a new technique called direction coding, typically to 1 to 2 bits per pixel.The geometric data is also useful in z-buffer image compositing and this new compression algorithm offers a very low storage overhead method for saving the information needed for z-buffer image compositing.The overall compression ratio of the new algorithm, including the geometric data overhead, is compared to conventional spatial linear prediction compression and is shown to be consistently better, by a factor of 1.4 or more, even with large frame-to-frame motion.