An image sensor is one of the most important building blocks of modern electronics, such as smartphones, drones, and autonomous vehicles. The state-of-the-art image sensor captures an incredibly detailed, high-resolution image [1]-[3], however, it also generates a large amount of data, which overwhelms the downstream processing. To address this challenge, recent works have investigated in- and near-sensor data compression techniques [4]-[14]. [4] is one of the few works which demonstrated the end-to-end systems (containing pixels, compression hardware, and analog-to-digital converters [ADC]). However, it consumes a significant amount of energy of 5919 pJ/pixel, of which the analog discrete cosine transform (DCT) processor takes 58%. On the other hand, [5] proposed the analog compressor and ADC systems (no pixels). However, it also consumes a non-negligible amount of energy (404 pJ/pixel) and exhibits a limited performance of only 6 frame-per-second (fps). In contrast, [6], [7] proposed digital DCT processors, which reduce the energy consumption down to 29-126 pJ/pixel. However, they require the analog-digital conversion of every pixel, which would significantly increase the overall energy consumption.
