Optimized OpenCL™ kernels for frequency domain image high-boost filters using image vectorization technique

Abstract

Image high boost filtering uses high-boost filters to enhance the quality of an image, which has also seen in remote sensing, satellite broadcasting, classroom monitoring, and many more real-time video processing applications and requires its faster implementation. OpenCL is a widely adapted parallel programming framework that provides core level data parallelism, and dedicated for heterogeneous parallel devices like from low cost DSP to high-end CPU, GPU and FPGA. In this article, we have considered mostly used Ideal, Gaussian, Butterworth, and Laplacian of Gaussian frequency domain high-boost filters and implemented channelized OpenCL kernels for their rapid execution. In addition to that, these kernels are modified using image vectorization technique to optimize their time utilization by reducing the execution time of these OpenCL kernels to half. At last, performance analysis is carried out for these two types of OpenCL kernel implementations to determine their effectiveness with regard to time consumption and accuracy. Here, different image performance evaluation metrics like entropy, standard deviation, mean absolute error, percentage fit error, SSIM, correlation, and peak signal to noise ratio are applied to measure rightness of the above high-boost filters. From the results, we have concluded that a vectorized Butterworth high-boost filter kernel is the suitable one to provide better results among those filters, which might be highly adaptable in time bound real-time applications using various embedded devices.