Performance comparison of lossless compression strategies for dynamic vision sensor data

Nabeel Khan; Khurram Iqbal; Maria Martini

doi:10.1109/ICASSP40776.2020.9053178

Performance comparison of lossless compression strategies for dynamic vision sensor data

Nabeel Khan
, Khurram Iqbal
, Maria Martini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Dynamic Vision Sensors (DVS) are emerging neuromorphic visual capturing devices, with great advantages in terms of low power consumption, wide dynamic range, and high temporal resolution in diverse applications. The capturing method results in lower data rates than conventional video. Still, such data can be further compressed. This is an emerging research area and a performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed. According to the detailed experimental analysis, LZMA achieves the best compression ratio among all the considered strategies. On the other hand, Brotli achieves the best trade-off between speed (compression and decompression) and compression ratio.

Original language	English
Title of host publication	Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9053178
Publication status	Published - May 2020

Bibliographical note

Note: Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315

This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)]

Organising Body: Institute of Electrical and Electronics Engineers

Organising Body: Institute of Electrical and Electronics Engineers

Keywords

Communication, cultural and media studies

Access to Document

10.1109/ICASSP40776.2020.9053178

https://doi.org/10.1109/ICASSP40776.2020.9053178

Performance comparison of lossless compression strategies for dynamic vision sensor data
Khan, N., Iqbal, K. & Martini, M., May 2020.
Research output: Contribution to conference › Paper › peer-review

Cite this

Khan, N., Iqbal, K., & Martini, M. (2020). Performance comparison of lossless compression strategies for dynamic vision sensor data. In Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers https://doi.org/10.1109/ICASSP40776.2020.9053178

Khan, Nabeel ; Iqbal, Khurram ; Martini, Maria. / Performance comparison of lossless compression strategies for dynamic vision sensor data. Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers. 2020.

@inproceedings{e7a6fba23b704ad680984dc1892c35ea,

title = "Performance comparison of lossless compression strategies for dynamic vision sensor data",

abstract = "Dynamic Vision Sensors (DVS) are emerging neuromorphic visual capturing devices, with great advantages in terms of low power consumption, wide dynamic range, and high temporal resolution in diverse applications. The capturing method results in lower data rates than conventional video. Still, such data can be further compressed. This is an emerging research area and a performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed. According to the detailed experimental analysis, LZMA achieves the best compression ratio among all the considered strategies. On the other hand, Brotli achieves the best trade-off between speed (compression and decompression) and compression ratio.",

keywords = "Communication, cultural and media studies",

author = "Nabeel Khan and Khurram Iqbal and Maria Martini",

note = "Note: Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers",

year = "2020",

month = may,

doi = "10.1109/ICASSP40776.2020.9053178",

language = "English",

booktitle = "Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers",

}

Khan, N, Iqbal, K & Martini, M 2020, Performance comparison of lossless compression strategies for dynamic vision sensor data. in Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP40776.2020.9053178

Performance comparison of lossless compression strategies for dynamic vision sensor data. / Khan, Nabeel; Iqbal, Khurram; Martini, Maria.
Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers. 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Performance comparison of lossless compression strategies for dynamic vision sensor data

AU - Khan, Nabeel

AU - Iqbal, Khurram

AU - Martini, Maria

N1 - Note: Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers

PY - 2020/5

Y1 - 2020/5

N2 - Dynamic Vision Sensors (DVS) are emerging neuromorphic visual capturing devices, with great advantages in terms of low power consumption, wide dynamic range, and high temporal resolution in diverse applications. The capturing method results in lower data rates than conventional video. Still, such data can be further compressed. This is an emerging research area and a performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed. According to the detailed experimental analysis, LZMA achieves the best compression ratio among all the considered strategies. On the other hand, Brotli achieves the best trade-off between speed (compression and decompression) and compression ratio.

AB - Dynamic Vision Sensors (DVS) are emerging neuromorphic visual capturing devices, with great advantages in terms of low power consumption, wide dynamic range, and high temporal resolution in diverse applications. The capturing method results in lower data rates than conventional video. Still, such data can be further compressed. This is an emerging research area and a performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed. According to the detailed experimental analysis, LZMA achieves the best compression ratio among all the considered strategies. On the other hand, Brotli achieves the best trade-off between speed (compression and decompression) and compression ratio.

KW - Communication, cultural and media studies

U2 - 10.1109/ICASSP40776.2020.9053178

DO - 10.1109/ICASSP40776.2020.9053178

M3 - Conference contribution

BT - Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers

ER -

Khan N, Iqbal K, Martini M. Performance comparison of lossless compression strategies for dynamic vision sensor data. In Published in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (2020). Piscataway : IEEE, pp.4427-4431. ISSN (Print) 1520-6149, ISSN (Electronic) 2379-190X. ISBN: 9781509066315 This work was supported by the Engineering and Physical Sciences Research Council [Grant Number: EP/P02271/5/1 The Internet of Silicon Retinas: Machine to machine communications for neuromorphic vision sensing data (IoSiRe)] Organising Body: Institute of Electrical and Electronics Engineers Organising Body: Institute of Electrical and Electronics Engineers. 2020 doi: 10.1109/ICASSP40776.2020.9053178

Performance comparison of lossless compression strategies for dynamic vision sensor data

Abstract

Bibliographical note

Keywords

Access to Document

Fingerprint

Research output

Performance comparison of lossless compression strategies for dynamic vision sensor data

Cite this