A content-based methodology for power-aware motion estimation architecture

This paper presents a novel power-aware motion estimation algorithm, called adaptive content-based subsample algorithm (ACSA), for battery-powered multimedia devices. While the battery status changes, the architecture adaptively performs graceful tradeoffs between power consumption and compression quality. As the available energy decreases, the algorithm raises the subsample rate for maximizing battery lifetime. Differing from the existing subsample algorithms, the content-based algorithm first extracts edge pixels from a macro-block and then subsamples the remaining low-frequency part. In this way, we can alleviate the aliasing problem and thus keep the quality degradation low as the subsample rate increases. As shown in experimental results, the architecture can dynamically operate at different power consumption modes with little quality degradation according to the remaining capacity of battery pack while the power overhead of edge extraction is under 0.8%.