A Novel Pipeline for Compressing Image Buffers in Remote Education Video Conferencing using Harris Corner Detection and Pixel Map Array

Abstract

The COVID-19 pandemic has compelled educational institutions worldwide to shift to remote online education. Addressing the growing trend, an Oxford University Press report titled ‘Addressing the Deepening Digital Divide’ states that poor digital access is the most significant barrier to digital learning according to 68 percent of academicians. Students in many remote parts of India frequently have access to limited bandwidth internet, which is insufficient for the modern standards of network-hogging online video conference software solutions. This paper provides an algorithmic compression of image buffers to aid low-cost remote online video education. This compression can be done by translating the teacher's blackboard images to pixel arrays projected on a canvas on the student's dashboard while the instructor constantly communicates via real-time voice. The image is first converted to grayscale and dilated with a square kernel. Using Harris Corner Detector, probable board corners are identified and compared to a geometrical center of the points and the corners recovered by cornerSubPix. An adaptive threshold is employed, distinguishing the board's contents from the backdrop on the cropped picture based on the recovered points. The pixel-mapped array is then transmitted to the students through the webRTC real-time protocol, which includes support for two-way audio, allowing the teacher to deliver lectures. Using Canvas API on the application front-end, the array is projected onto the student's device as a dot matrix display. This paper has achieved an effective rate in the video transmission format, aiding online remote education on low-bandwidth network devices.