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Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression.

Jayasingam Adhuran1, Nabeel Khan2, Maria G Martini1

  • 1Faculty of Engineering, Computing, and the Environment, Kingston University London, Penrhyn Rd., Kingston upon Thames KT1 2EE, UK.

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|March 13, 2024
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Summary
This summary is machine-generated.

A new method, Time-Aggregated Lossless Encoding of Events based on Point-Cloud Compression (TALEN-PCC), enhances data compression for Neuromorphic Vision Sensors (NVSs). This approach offers better compression ratios, especially for simpler scenes and longer time intervals.

Keywords:
neuromorphic spike eventsneuromorphic vision sensor (NVS)point-cloud compressionsilicon retinasspike encoding

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Area of Science:

  • Computer Vision
  • Data Compression
  • Sensor Technology

Background:

  • Neuromorphic Vision Sensors (NVSs) offer advantages like low power and high dynamic range over traditional cameras.
  • NVS data, while inherently lower in data rate, can be further compressed for efficient storage and transmission.
  • Previous methods like Time Aggregation-based Lossless Video Encoding for Neuromorphic Vision Sensor Data (TALVEN) have been explored.

Purpose of the Study:

  • To introduce and evaluate a novel compression strategy for NVS data.
  • To compare the effectiveness of the new strategy against prior methods.
  • To analyze the impact of scene complexity and time aggregation intervals on compression performance.

Main Methods:

  • Leveraging time aggregation of NVS events.
  • Encoding time-aggregated data using point-cloud compression techniques.
  • Implementing the Time-Aggregated Lossless Encoding of Events based on Point-Cloud Compression (TALEN-PCC) strategy.

Main Results:

  • TALEN-PCC demonstrates superior compression ratios compared to the TALVEN strategy on the tested dataset.
  • Compression gains are most significant in low-event rate and low-complexity scenes.
  • Higher compression is achieved with time aggregation intervals exceeding 5 ms, though gains diminish compared to state-of-the-art for intervals under 5 ms.

Conclusions:

  • TALEN-PCC represents an effective advancement in lossless compression for NVS data.
  • The method's performance is sensitive to scene characteristics and temporal aggregation settings.
  • Further research may optimize TALEN-PCC for various NVS applications and data types.