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.
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Berry, I., Wilson, J., Mayo, C., Diprose, J., Esnouf, R. (2004). The Effect of Image Compression on Classification and Storage Requirements in a High-Throughput Crystallization System. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_17
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DOI: https://doi.org/10.1007/978-3-540-28651-6_17
Publisher Name: Springer, Berlin, Heidelberg
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