.png){kind=icon}
返回
Flow-Based Visual Stream Compression for Event Cameras
.png){kind=icon}
OA
AI
DOI：10.1109/JIOT.2024.3450428.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As the use of neuromorphic, event-based vision sensors expands, the need for compression of their output streams has increased. While their operational principle ensures event streams are spatially sparse, the high-temporal resolution of the sensors can result in high-data rates from the sensor depending on scene dynamics. For systems operating in communication-bandwidth-constrained and power-constrained environments, it is essential to compress these streams before transmitting them to a remote receiver. Therefore, we introduce a flow-based method for the real-time asynchronous compression of event streams as they are generated. This method leverages real-time optical flow estimates to predict future events without needing to transmit them, therefore, drastically reducing the amount of data transmitted. The flow-based compression introduced is evaluated using a variety of methods, including spatiotemporal distance between event streams. The introduced method itself is shown to achieve an average compression ratio (CR) of 2.70 on a variety of event-camera data sets with the evaluation configuration used. That compression is achieved with a median temporal error of 0.31 ms and an average spatiotemporal event-stream distance of 4.72. When combined with Lempel-Ziv-Markov chain algorithm compression for non-real-time applications, our method can achieve state-of-the-art average CRs ranging from 9.29 to 13.01. Additionally, we demonstrate that the proposed prediction algorithm is capable of performing real time, low-latency event prediction.
Keyword:
Optical flow
Encoding
Vision sensors
Image coding
Real-time systems
Optical losses
Streaming media
Compression
event-based
low power
neuromorphic
optical flow
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Disruption of integrin-dependent adhesion and survival of endothelial cells by recombinant adenovirus expressing isolated β integrin cytoplasmic domains.png)
Gene Therapy
Gene TherapyIF0
PREAI
分享收藏Lossless Compression of Neuromorphic Vision Sensor Data Based on Point Cloud Representation基于点云表示的神经形态视觉传感器数据无损压缩IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM事件相机数据集和模拟器: 用于姿态估计，视觉里程计和SLAM的基于事件的数据
OAAI
OAAI分享收藏Two new species of Chlorospatha section Orientales (Araceae) from western Andes in Colombia来自哥伦比亚西部安第斯山脉的 Chlorospatha section Orientales (Araceae) 两个新种PhytoKeysOAAI
PhytoKeysIF0
OAAI分享收藏Time-Aggregation-Based Lossless Video Encoding for Neuromorphic Vision Sensor Data基于时间聚合的神经形态视觉传感器数据无损视频编码OAAI
OAAI分享收藏
查看更多内容