Skip to Main Content
We study scalable image and video coding for the surveillance of rooms and personal environments based on inexpensive cameras and portable devices. The scalability is achieved through a multi-level 2D dyadic wavelet decomposition featuring an accurate low-cost integer wavelet implementation with lifting. As our primary contribution, we present a modification to the SPECK wavelet coefficient encoding algorithm to significantly improve the efficiency of an embedded system implementation. The modification consists of storing the significance of all quadtree nodes in a buffer, where each node comprises several coefficients. This buffer is then used to efficiently construct the code with minimal and direct memory access. Our approach allows efficient parallel implementation on multi-core computer systems and gives a substantial reduction of memory access and thus power consumption. We report experimental results, showing an approximate gain factor of 1,000 in execution time compared to a straightforward SPECK implementation, when combined with code optimization on a common digital signal processor. This translates to 75 full color 4CIF 4:2:0 encoding cycles per second, clearly demonstrating the realtime capabilities of the proposed modification.