Analysis of Input-Dependent Noise in Self-Timed Reset Dynamic Vision Sensor and its Impact on Data Quality

Abstract

Threshold temporal contrast (TTC), as a key feature of event-based signal encoding scheme, always fluctuates due to the various noise sources in circuit implementation. This fluctuation (or noise) distorts the information that each event contains, leading to the data quality degeneration. To avoid the errors that may appear in the subsequent signal processing, the degeneration needs to be within the limit demanded by the algorithm. Therefore, modeling the relationship between noise and data quality is important for the event-based hardware application. This paper focuses on analyzing the input-dependent noise on TTC caused by dark current and charge injection, which are labeled as $\delta \Theta _{\mathrm {dc}}$ and $\delta \Theta _{\mathrm {ci}}$ respectively, in self-timed reset dynamic vision sensors (STR-DVS), and evaluates the corresponding impacts on data quality. As for the theoretical derivation part, $\delta \Theta _{\mathrm {dc}}$ is calculated using a logarithmic photocurrent-to-voltage model with dark current taken into account, and $\delta \Theta _{\mathrm {ci}}$ is figured out with a three-phase on-to-off-transient switch model applied in differencing circuit. The data quality evaluation is implemented with event clusters generated from the nonideality-contained model, and the signal-to-distortion ratio (SDR) of the signal reconstructed by a perfect nonlinear recovery algorithm is taken as the metric. The analysis and simulation results are expected to offer a reference on assessing the applicability of STR-DVS for a specific application, or system parameter settings to make the data quality satisfy the requirement while using the sensor.