Efficient and lightweight quantized compressive sensing using μ-law

Abstract

IoT devices for video sensing need to operate within the constraints of limited bandwidth and low computing capabilities. To that effect, Compressive Sensing (CS) emerged as a prominent technique for balancing the quality of images/video and the computing/communication overheads. For CS of video data, the Block-based CS (BCS) is typically used due to low complexity. However, while CS reduces the number of samples to be transmitted, the bit-width of each sample increases due to the linear algebraic operations involved in CS, thus making CS less attractive in its pure and straightforward form. To further optimize the use of CS in IoT devices for video sensing, we explore the use of μ-law quantization technique due to its low hardware implementation overhead. We designed and implemented a complete CS platform with the integration of μ-law quantization, and studied the image quality at different compression ratios. The results show that the proposed quantization technique requires only up to 40 additional LUTs compared to the baseline algorithm, while achieving an additional compression of up to 280% in the best case.