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Feature Representation and Compression Methods for Event-Based Data
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AI
DOI：10.1109/JSEN.2023.3237754.png)
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Abstract
En 中文
camera emerges as a new type of neuromorphic sensor that records scenes in an asynchronous paradigm which provides high temporal resolution and high dynamic range. The huge volume of event-based data brings a computational burden to data transmission and real-time interaction in IntelliSense applications. Compared with the general encoding method and current event-based data compression method Spike Coding, we successively propose two event-based data compression methods by analyzing the statistical features of the event characteristic parameters (x, y, t, and f). Based on the data features of event parameters, we design the characteristic parameter jointed coding (CPJC) method with the idea of fixed-length coding, which reduces the redundancy of delimiters between modules. As for the limitation of the CPJC method in pixel scale, we further propose the ASCII coding based on bit operation (ACBO) method based on the data storage principle in computer memory, reducing the redundancy caused by data storage. We introduce compression coefficient K as the metric to evaluate the compression performance on a real-world dataset. And the metrics K of the proposed two methods are improved by 17.93% and 14.92%, respectively, compared with the current event-based compression method. The proposed ACBO method also eliminates the limitation in data pixel scale and has certain adoption for event noise and event dispersion in the scene. [GRAPHICS]
Keywords:
Data compression
Shape
Image coding
event camera
event-based vision
feature representation
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W