Microarray Image Lossless Compression Using General Entropy Coders and Image Compression Standards
Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the im...
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| Published in: | Circuits, systems, and signal processing Vol. 42; no. 8; pp. 5013 - 5040 |
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| Main Authors: | , |
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| Language: | English |
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01.08.2023
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| ISSN: | 0278-081X, 1531-5878 |
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| Abstract | Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image’s per bit-plane entropy profile is calculated to automatically determine a better threshold
T
to split the bit-planes into the foreground and background sub-images.
T
is initially set to 8. However, in algorithm 1,
T
is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. |
|---|---|
| AbstractList | Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image’s per bit-plane entropy profile is calculated to automatically determine a better threshold
T
to split the bit-planes into the foreground and background sub-images.
T
is initially set to 8. However, in algorithm 1,
T
is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image’s per bit-plane entropy profile is calculated to automatically determine a better threshold T to split the bit-planes into the foreground and background sub-images. T is initially set to 8. However, in algorithm 1, T is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. |
| Author | Joseph, Steffy Maria Sathidevi, P. S. |
| Author_xml | – sequence: 1 givenname: Steffy Maria orcidid: 0000-0002-6047-0931 surname: Joseph fullname: Joseph, Steffy Maria email: steffymaria89@gmail.com organization: Electronics and Communication Department, National Institute of Technology, Calicut – sequence: 2 givenname: P. S. orcidid: 0000-0003-3839-9910 surname: Sathidevi fullname: Sathidevi, P. S. organization: Electronics and Communication Department, National Institute of Technology, Calicut |
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| Cites_doi | 10.1016/j.patcog.2006.02.019 10.1016/j.csi.2014.12.001 10.1147/rd.232.0149 10.1145/214762.214771 10.1109/TNB.2003.816230 10.1007/s11760-008-0059-2 10.1109/TMI.2008.929095 10.1109/JRPROC.1952.273898 10.1007/978-1-4615-0799-4 10.1109/TBME.2005.869782 10.1002/j.1538-7305.1948.tb01338.x 10.1109/DCC.2012.11 10.1109/ICIP.2007.4379164 10.1201/9781315215785 10.1109/ICIP.2009.5413629 10.1109/HONET.2017.8102221 10.1109/ICIP.2006.312802 10.1109/CCP.2011.21 10.1109/ACSSC.2003.1292235 |
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| Keywords | JPEG 2000 Microarray images Lossless compression Entropy Pixel intensity reindexing Arithmetic coding Huffman coding Splitting bit-planes |
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| Title | Microarray Image Lossless Compression Using General Entropy Coders and Image Compression Standards |
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