Video compression with embedded wavelet coding and singularity maps

We consider the problem of real-time video compression for high-performance wireless information systems, particularly for military applications. Our approach is based on embedded coding of wavelet coefficients. Since this compresses data in order of visual importance, the bitstream can be truncated at any point, allowing a straightforward tradeoff between bitrate and picture quality. For lower bitrates, we propose the inclusion of singularity maps, which are a generalization of edge maps. These singularity maps help identify regions of interest in the video, so that they can be protected while the background is aggressively compressed. To lower costs and increase flexibility, we investigate purely software implementations running on general-purpose processors. Because of the availability of today's fast processors, designed for multimedia and communications applications, such an approach becomes more feasible. To this end we apply integer-to-integer wavelet transforms in order to avoid expensive floating-point operations. We also avoid motion compensation, which requires the expensive computation of motion vectors.