scheduling and energy-distortion tradeoffs with operational refinement of image processing
Ubiquitous image processing tasks (such as transform decompositions, filtering and motion estimation) do not currently provide graceful degradation when their clock-cycles budgets are reduced, e.g. when delay deadlines are imposed in a multi-tasking environment to meet throughput requirements. This is an important obstacle in the quest for full utilization of modern programmable platforms' capabilities, since: ( i ) worst-case considerations must be in place for reasonable quality of results; ( ii ) throughput-distortion tradeoffs are not possible for distortion-tolerant image processing applications without cumbersome (and potentially costly) system customization. In this paper, we extend the functionality of the recently-proposed software framework for operational refinement of image processing (ORIP) and demonstrate its inherent throughput-distortion and energy-distortion scalability. Importantly, our extensions allow for such scalabilities at the software level, without needing hardware-specific customization. Extensive tests on a mainstream notebook computer and on OLPC's subnotebook ("xo-laptop") verify that the proposed designs provide for: ( i ) seamless quality-complexity scalability per video frame; ( ii ) up to 60\% increase in processing throughput with graceful degradation in output quality; ( iii ) up to 20\% more images captured and filtered for the same power-level reduction on the xo-laptop.
programmable platforms energy-distortion scalability incremental refinement of computation throughput-distortion scalability