The effect of image compression on classification and storage requirements in a high-throughput crystallization system

Ian Berry; Jon Diprose; Robert Esnouf; Chris Mayo; Julie C. Wilson

doi:10.1007/978-3-540-28651-6_17

The effect of image compression on classification and storage requirements in a high-throughput crystallization system

Ian Berry, Jon Diprose, Robert Esnouf, Chris Mayo, Julie C. Wilson

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

Abstract

High-throughput crystallization and imaging facilities can require a huge amount of disk space to keep images on-line. Although compressed images can look very similar to the human eye, the effect on the performance of crystal detection software needs to be analysed. This paper tests the use of common lossy and lossless compression algorithms on image file size and on the performance of the York University image analysis software by comparison of compressed Oxford images with their native, uncompressed bitmap images. This study shows that significant (approximately 4-fold) space savings can be gained with only a moderate effect on classification capability.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science
Subtitle of host publication	5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings.
Editors	Zheng Yang, Hujun Yin, Richard Everson
Publisher	Springer
Pages	117-124
Number of pages	8
Volume	3177
Edition	2004
ISBN (Print)	978-3-540-22881-3
DOIs	https://doi.org/10.1007/978-3-540-28651-6_17
Publication status	Published - 29 Oct 2004
Event	5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004) - Execter Duration: 25 Aug 2004 → 27 Aug 2004

Conference

Conference	5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004)
City	Execter
Period	25/08/04 → 27/08/04

Keywords

AUTOMATIC CLASSIFICATION
TRIALS

Access to Document

10.1007/978-3-540-28651-6_17

http://www.springerlink.com/content/mwv7mgaxf9tdnb36/fulltext.pdf

Cite this

Berry, I., Diprose, J., Esnouf, R., Mayo, C., & Wilson, J. C. (2004). The effect of image compression on classification and storage requirements in a high-throughput crystallization system. In Z. Yang, H. Yin, & R. Everson (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings. (2004 ed., Vol. 3177, pp. 117-124). Springer. https://doi.org/10.1007/978-3-540-28651-6_17

Berry, Ian ; Diprose, Jon ; Esnouf, Robert et al. / The effect of image compression on classification and storage requirements in a high-throughput crystallization system. Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings.. editor / Zheng Yang ; Hujun Yin ; Richard Everson. Vol. 3177 2004. ed. Springer, 2004. pp. 117-124

@inproceedings{2705363b909440228472231088077004,

title = "The effect of image compression on classification and storage requirements in a high-throughput crystallization system",

abstract = "High-throughput crystallization and imaging facilities can require a huge amount of disk space to keep images on-line. Although compressed images can look very similar to the human eye, the effect on the performance of crystal detection software needs to be analysed. This paper tests the use of common lossy and lossless compression algorithms on image file size and on the performance of the York University image analysis software by comparison of compressed Oxford images with their native, uncompressed bitmap images. This study shows that significant (approximately 4-fold) space savings can be gained with only a moderate effect on classification capability.",

keywords = "AUTOMATIC CLASSIFICATION, TRIALS",

author = "Ian Berry and Jon Diprose and Robert Esnouf and Chris Mayo and Wilson, {Julie C.}",

year = "2004",

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booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science",

publisher = "Springer",

address = "Germany",

edition = "2004",

note = "5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004) ; Conference date: 25-08-2004 Through 27-08-2004",

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Berry, I, Diprose, J, Esnouf, R, Mayo, C & Wilson, JC 2004, The effect of image compression on classification and storage requirements in a high-throughput crystallization system. in Z Yang, H Yin & R Everson (eds), Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings.. 2004 edn, vol. 3177, Springer, pp. 117-124, 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), Execter, 25/08/04. https://doi.org/10.1007/978-3-540-28651-6_17

The effect of image compression on classification and storage requirements in a high-throughput crystallization system. / Berry, Ian; Diprose, Jon; Esnouf, Robert et al.
Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings.. ed. / Zheng Yang; Hujun Yin; Richard Everson. Vol. 3177 2004. ed. Springer, 2004. p. 117-124.

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

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AB - High-throughput crystallization and imaging facilities can require a huge amount of disk space to keep images on-line. Although compressed images can look very similar to the human eye, the effect on the performance of crystal detection software needs to be analysed. This paper tests the use of common lossy and lossless compression algorithms on image file size and on the performance of the York University image analysis software by comparison of compressed Oxford images with their native, uncompressed bitmap images. This study shows that significant (approximately 4-fold) space savings can be gained with only a moderate effect on classification capability.

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Berry I, Diprose J, Esnouf R, Mayo C, Wilson JC. The effect of image compression on classification and storage requirements in a high-throughput crystallization system. In Yang Z, Yin H, Everson R, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2004, Lecture Notes in Computer Science: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings.. 2004 ed. Vol. 3177. Springer. 2004. p. 117-124 doi: 10.1007/978-3-540-28651-6_17