Re-Interpreting the Step-Response Probability Curve to Extract Fundamental Physical Parameters of Event-based Vision Sensors

TL;DR

This paper improves the measurement and interpretation of the step-response probability curve (S-curve) in event-based vision sensors, enabling accurate extraction of physical parameters and correcting previous misconceptions about contrast threshold behavior.

Contribution

It introduces robust techniques for generating accurate S-curves, corrects prior misinterpretations of contrast threshold variation, and demonstrates how to infer key physical parameters from S-curves.

Findings

Accurate S-curves can be generated by decoupling second-order effects.

Correct interpretation shows contrast threshold $ heta$ does not vary with illumination.

Physical parameters like dark current and noise can be inferred from S-curves.

Abstract

Biologically inspired event-based vision sensors (EVS) are growing in popularity due to performance benefits including ultra-low power consumption, high dynamic range, data sparsity, and fast temporal response. They efficiently encode dynamic information from a visual scene through pixels that respond autonomously and asynchronously when the per-pixel illumination level changes by a user-selectable contrast threshold ratio, $\theta$. Due to their unique sensing paradigm and complex analog pixel circuitry, characterizing Event-based Vision Sensor (EVS) is non-trivial. The step-response probability curve (S-curve) is a key measurement technique that has emerged as the standard for measuring $\theta$. In this work, we detail the method for generating accurate S-curves by applying an appropriate stimulus and sensor configuration to decouple 2nd-order effects from the parameter being studied. We use an EVS pixel simulation to demonstrate how noise and other physical constraints can lead to error in the measurement, and develop two techniques that are robust enough to obtain accurate estimates. We then apply best practices derived from our simulation to generate S-curves for the latest generation Sony IMX636 and interpret the resulting family of curves to correct the apparent anomalous result of previous reports suggesting that $\theta$ changes with illumination. Further, we demonstrate that with correct interpretation, fundamental physical parameters such as dark current and RMS noise can be accurately inferred from a collection of S-curves, leading to more accurate parameterization for high-fidelity EVS simulations.