Deep Learning-based Event Data Coding: A Joint Spatiotemporal and Polarity Solution
This work addresses the problem of high data volume from neuromorphic sensors for applications requiring speed and low latency, offering an incremental improvement over prior lossless and point cloud-based coding approaches.
The paper tackles efficient coding for event camera data by proposing a deep learning-based joint spatiotemporal and polarity solution, achieving significant compression gains compared to existing methods while maintaining performance in computer vision tasks like event classification.
Neuromorphic vision sensors, commonly referred to as event cameras, have recently gained relevance for applications requiring high-speed, high dynamic range and low-latency data acquisition. Unlike traditional frame-based cameras that capture 2D images, event cameras generate a massive number of pixel-level events, composed by spatiotemporal and polarity information, with very high temporal resolution, thus demanding highly efficient coding solutions. Existing solutions focus on lossless coding of event data, assuming that no distortion is acceptable for the target use cases, mostly including computer vision tasks. One promising coding approach exploits the similarity between event data and point clouds, thus allowing to use current point cloud coding solutions to code event data, typically adopting a two-point clouds representation, one for each event polarity. This paper proposes a novel lossy Deep Learning-based Joint Event data Coding (DL-JEC) solution adopting a single-point cloud representation, thus enabling to exploit the correlation between the spatiotemporal and polarity event information. DL-JEC can achieve significant compression performance gains when compared with relevant conventional and DL-based state-of-the-art event data coding solutions. Moreover, it is shown that it is possible to use lossy event data coding with its reduced rate regarding lossless coding without compromising the target computer vision task performance, notably for event classification. The use of novel adaptive voxel binarization strategies, adapted to the target task, further enables DL-JEC to reach a superior performance.