TY - GEN
T1 - LOSSY EVENT COMPRESSION BASED ON IMAGE-DERIVED QUAD TREES AND POISSON DISK SAMPLING
AU - Banerjee, Srutarshi
AU - Wang, Zihao W.
AU - Chopp, Henry H.
AU - Cossairt, Oliver
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
This work was supported in part by a DARPA Contract No. 17-2-0044.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Event cameras have provided new opportunities for tackling visual tasks under challenging scenarios over conventional RGB cameras. However, not much focus has been given on event compression algorithms. The main challenge for compressing events is its unique asynchronous form. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our algorithm achieves greater than 6× higher compression compared to the state of the art.
AB - Event cameras have provided new opportunities for tackling visual tasks under challenging scenarios over conventional RGB cameras. However, not much focus has been given on event compression algorithms. The main challenge for compressing events is its unique asynchronous form. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our algorithm achieves greater than 6× higher compression compared to the state of the art.
KW - Lossy event compression
KW - Poisson disk sampling
KW - Quad tree segmentation
UR - http://www.scopus.com/inward/record.url?scp=85125596422&partnerID=8YFLogxK
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U2 - 10.1109/ICIP42928.2021.9506546
DO - 10.1109/ICIP42928.2021.9506546
M3 - Conference contribution
AN - SCOPUS:85125596422
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2154
EP - 2158
BT - 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PB - IEEE Computer Society
T2 - 2021 IEEE International Conference on Image Processing, ICIP 2021
Y2 - 19 September 2021 through 22 September 2021
ER -