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Custom Lossless Compression and High-Quality Lossy Compression of White Blood Cell Microscopy Images for Display and Machine Learning Applications

Nilsson, Terese and Hamngren, Daniel LU (2013) In Master's Theses in Mathematical Sciences FMA820 20131
Mathematics (Faculty of Engineering)
Abstract
This master's thesis investigates both custom lossless compression and high-quality lossy compression of microscopy images of white blood cells produced by CellaVision's blood analysis systems. A number of different compression strategies have been developed and evaluated, all of which are taking advantage of the specific color filter array used in the sensor in the cameras in the analysis systems. Lossless compression has been the main focus of this thesis.

The lossless compression method, of those developed, that gave best result is based on a statistical autoregressive model.
A model is constructed for each color channel with external information from the other color channels. The difference between the predictions from the... (More)
This master's thesis investigates both custom lossless compression and high-quality lossy compression of microscopy images of white blood cells produced by CellaVision's blood analysis systems. A number of different compression strategies have been developed and evaluated, all of which are taking advantage of the specific color filter array used in the sensor in the cameras in the analysis systems. Lossless compression has been the main focus of this thesis.

The lossless compression method, of those developed, that gave best result is based on a statistical autoregressive model.
A model is constructed for each color channel with external information from the other color channels. The difference between the predictions from the statistical model and the original is further Huffman coded. The method achieves an average bit-rate of 3.0409 bits per pixel on the test set consisting of 604 images.

The proposed lossy method is based on taking the difference between the image compressed with an ordinary lossy compression method, JPEG 2000, and the original image. The JPEG 2000 image is saved, as well as the differences at the foreground (i.e. locations with cells), in order to keep the cells identical to the cells in the original image, but allow loss of information for the, not so important, background. This method achieves a bit-rate of 2.4451 bits per pixel, with a peak signal-to-noise-ratio (PSNR) of 48.05 dB. (Less)
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author
Nilsson, Terese and Hamngren, Daniel LU
supervisor
organization
alternative title
Förlustfri komprimering och högkvalitativ förstörande komprimering av blodcellsbilder för bildvisning och maskininlärning
course
FMA820 20131
year
type
H2 - Master's Degree (Two Years)
subject
keywords
image compression, compression of medical images, blood cell microscopy, pca, regression analysis, discrete wavelet transform, reversible integer transform, lossy, lossless, lossy compression, lossless compression, Huffman, Huffman coding, image analysis, image processing, CFA, color filter array, bayer filter
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3249-2013
ISSN
1404-6342
other publication id
2013: E32
language
English
id
3810913
date added to LUP
2013-09-20 12:15:25
date last changed
2013-09-20 12:15:25
@misc{3810913,
  abstract     = {This master's thesis investigates both custom lossless compression and high-quality lossy compression of microscopy images of white blood cells produced by CellaVision's blood analysis systems. A number of different compression strategies have been developed and evaluated, all of which are taking advantage of the specific color filter array used in the sensor in the cameras in the analysis systems. Lossless compression has been the main focus of this thesis.

The lossless compression method, of those developed, that gave best result is based on a statistical autoregressive model. 
A model is constructed for each color channel with external information from the other color channels. The difference between the predictions from the statistical model and the original is further Huffman coded. The method achieves an average bit-rate of 3.0409 bits per pixel on the test set consisting of 604 images. 

The proposed lossy method is based on taking the difference between the image compressed with an ordinary lossy compression method, JPEG 2000, and the original image. The JPEG 2000 image is saved, as well as the differences at the foreground (i.e. locations with cells), in order to keep the cells identical to the cells in the original image, but allow loss of information for the, not so important, background. This method achieves a bit-rate of 2.4451 bits per pixel, with a peak signal-to-noise-ratio (PSNR) of 48.05 dB.},
  author       = {Nilsson, Terese and Hamngren, Daniel},
  issn         = {1404-6342},
  keyword      = {image compression,compression of medical images,blood cell microscopy,pca,regression analysis,discrete wavelet transform,reversible integer transform,lossy,lossless,lossy compression,lossless compression,Huffman,Huffman coding,image analysis,image processing,CFA,color filter array,bayer filter},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Custom Lossless Compression and High-Quality Lossy Compression of White Blood Cell Microscopy Images for Display and Machine Learning Applications},
  year         = {2013},
}