Lossless compression of data from static and mobile Dynamic Vision Sensors : performance and trade-offs
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-30-1362
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2020.2996661
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 103149
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Dynamic Vision Sensors (DVS) have the potential to disrupt the area of Internet of Things, enabling the capture of a scene with a limited bit-rate. As part of the “Internet of Silicon Retinas (IoSiRe)” project funded by EPSRC, this paper aims at reducing the data rate. Here, we adapt existing compression methods (not usable directly on DVS data) to DVS data and evaluate their performance.
The ability to select the best compression strategy is significant as it is relevant to all applications requiring the transmission of DVS data, e.g., IoT and autonomous driving.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -