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.