Analysis-preserving video microscopy compression via correlation and mathematical morphology

ABSTRACT The large amount video data produced by multi‐channel, high‐resolution microscopy system drives the need for a new high‐performance domain‐specific video compression technique. We describe a novel compression method for video microscopy data. The method is based on Pearson's correlatio...

Full description

Saved in:
Bibliographic Details
Published in:Microscopy research and technique 2015-12, Vol.78 (12), p.1055-1061
Main Authors: Shao, Chong, Zhong, Alfred, Cribb, Jeremy, Osborne, Lukas D., O'Brien III, E. Timothy, Superfine, Richard, Mayer-Patel, Ketan, Taylor II, Russell M.
Format: Article
Language:English
Subjects:
Algorithms
biomedical image processing
Compressed
Data compression
image analysis
Lossless
Mathematical analysis
Mathematical morphology
Microscopy
Video data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:ABSTRACT The large amount video data produced by multi‐channel, high‐resolution microscopy system drives the need for a new high‐performance domain‐specific video compression technique. We describe a novel compression method for video microscopy data. The method is based on Pearson's correlation and mathematical morphology. The method makes use of the point‐spread function (PSF) in the microscopy video acquisition phase. We compare our method to other lossless compression methods and to lossy JPEG, JPEG2000, and H.264 compression for various kinds of video microscopy data including fluorescence video and brightfield video. We find that for certain data sets, the new method compresses much better than lossless compression with no impact on analysis results. It achieved a best compressed size of 0.77% of the original size, 25× smaller than the best lossless technique (which yields 20% for the same video). The compressed size scales with the video's scientific data content. Further testing showed that existing lossy algorithms greatly impacted data analysis at similar compression sizes. Microsc. Res. Tech. 78:1055–1061, 2015. © 2015 Wiley Periodicals, Inc.
ISSN:1059-910X
1097-0029
DOI:10.1002/jemt.22584