A quality-aware Energy-scalable Gaussian Smoothing Filter for image processing applications

Energy-efficient design is the prime requirement for modern portable devices as these devices employ compute intensive image/video processing cores which produces output for human consumption. The limited perception of human sense can be exploited to improve energy-efficacy via approximate designs. In this paper, a novel quality-aware Energy-Scalable Gaussian Smoothing Filter (ES-GSF) is proposed that significantly reduces energy requirement at the cost of slightly reduced quality. The energy scalability within ES-GSF is achieved by exploiting the relative significance of kernel coefficients existing on different boundaries. The ES-GSF computes significant boundaries for the given energy budget. Simulation results show that ES-GSF consumes 30.46% reduced energy with graceful quality degradation over the well-known existing architectures. Further, the ES-GSF can scale energy up to 65.05% when switched from high quality mode to energy-efficient mode. The efficacy of the proposed filter is demonstrated in edge detection where ES-GSF embedded edge detectors consume 29.9% less energy over the well-known existing architectures.