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.
Microprocess. Microsystems