A novel pipelined architecture of entropy filter

In computer vision, entropy is a measure adopted to characterize the texture information of a grayscale image, and an entropy filter is a fundamental operation used to calculate local entropy. However, this filter is computationally intensive and demands an efficient means of implementation. Additionally, with the foreseeable end of Moore’s law, there is a growing trend towards hardware offloading to increase computing power. In line with this trend, we propose a novel method for the calculation of local entropy and introduce a corresponding pipelined architecture. Under the proposed method, a sliding window of pixels undergoes three steps: sorting, adjacent difference calculation, and pipelined entropy calculation. Compared with a conventional design, implementation results on a Zynq UltraScale+ XCZU7EV-2FFVC1156 MPSoC device demonstrate that our pipelined architecture can reach a maximum throughput of handling 764.526 megapixels per second while achieving 2.4 × and 2.9 × reductions in resource utilization and 1.1 × reduction in power consumption.