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Export Reference (APA) 
Sezavar, A., Brites, C. & Ascenso, J. (2024). Learning-based lossless event data compression. In 2024 IEEE International Conference on Visual Communications and Image Processing, VCIP 2024. Tokyo: IEEE.
Export Reference (IEEE) 
A. Sezavar et al.,  "Learning-based lossless event data compression", in 2024 IEEE Int. Conf. on Visual Communications and Image Processing, VCIP 2024, Tokyo, IEEE, 2024
Export BibTeX 
@inproceedings{sezavar2024_1782684097317,
	author = "Sezavar, A. and Brites, C. and Ascenso, J.",
	title = "Learning-based lossless event data compression",
	booktitle = "2024 IEEE International Conference on Visual Communications and Image Processing, VCIP 2024",
	year = "2024",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/VCIP63160.2024.10849853",
	publisher = "IEEE",
	address = "Tokyo",
	organization = "",
	url = "https://www.vcip2024.org"
}
Export RIS 
TY  - CPAPER
TI  - Learning-based lossless event data compression
T2  - 2024 IEEE International Conference on Visual Communications and Image Processing, VCIP 2024
AU  - Sezavar, A.
AU  - Brites, C.
AU  - Ascenso, J.
PY  - 2024
SN  - 1018-8770
DO  - 10.1109/VCIP63160.2024.10849853
CY  - Tokyo
UR  - https://www.vcip2024.org
AB  - Emerging event cameras acquire visual information by detecting time domain brightness changes asynchronously at the pixel level and, unlike conventional cameras, are able to provide high temporal resolution, very high dynamic range, low latency, and low power consumption. Considering the huge amount of data involved, efficient compression solutions are very much needed. In this context, this paper presents a novel deep-learning-based lossless event data compression scheme based on octree partitioning and a learned hyperprior model. The proposed method arranges the event stream as a 3D volume and employs an octree structure for adaptive partitioning. A deep neural network-based entropy model, using a hyperprior, is then applied. Experimental results demonstrate that the proposed method outperforms traditional lossless data compression techniques in terms of compression ratio and bits per event.
ER  -