Optimizing RGB to Grayscale, Gaussian Blur and Sobel-Filter operations on FPGAs for reduced dynamic power consumption

Abstract: The conversion of pixels from their RGB to Grayscale formats is a crucial first step in numerous Image Pre-Processing, Computer Vision, and as highlighted here, edge detection modules. This paper presents an implementation of the Shift-Add Multiplication algorithm for efficient constant multiplications of the NTSC formula weights for RGB to Grayscale conversion on FPGAs. The proposed module is designed to be reconfigurable to both fixed-point and floating-point formats, providing flexibility in precision and resource utilization based on application requirements. Additionally, a Python script was developed to automate the generation of Verilog code for fractional constant multiplications, as proposed in this study. Pipelined modules for Gaussian Blur and the Sobel-Filter were also designed to enable the development of a complete real-time edge detection system on FPGAs. The findings reveal that Shift-Add algorithm based multiplier’s significantly reduce dynamic power consumption as compared to the use of the built-in DSP blocks on FPGA boards while performing constant multiplications for RGB to Grayscale conversion.