An analog-to-information converter (AIC) enables information quantization instead of signal quantization, which can reduce both quantization efforts and data bandwidth. Therefore, an AIC will relax data processing and transmission burden and improve overall power efficiency, making it attractive for wireless vision sensor networks. In this paper, we propose a background subtraction based column-parallel AIC for motion-triggered vision sensors. It features low-power and robust background subtraction for motion extraction as well as efficient information quantization. The AIC is implemented by integrating background subtraction with a successive-approximation-register and single-lope (SAR-SS) hybrid ADC using a new architecture. Targeting at scene interpretation applications, 6-bit background and 8-bit foreground are adopted. Simulation results show that vision data bandwidth and ADC power are reduced by 85.23% and 95.88% respectively when full-bit-depth motion images are required. The bandwidth reduction and the ADC power reduction can be further improved to 87.50% and 98.48% respectively if only binary motion images are needed.