Splitting bits for lossless compression of microarray images

Basar Koc, Ziya Arnavut, Dilip Sarkar, Huseyin Kocak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In an earlier publication we reported on the effectiveness of the Burrows-Wheeler transformation followed by inversion coder (BWIC) in the lossless compression of DNA microarray images where we obtained gains of average 6.5% over generic image compressors. In this work, we propose an enhancement of our previous technique by exploiting the bit distribution of images. Using a simple statistical test, we first decide if it will be gainful to split a 16-bit microarray image into two 8-bit images. In case of splitting, it turns out that the first 8-bit image is highly compressible and we use BWIC to compress it. The second 8-bit image most often contains noise and the bit distribution can become nearly random. We use the Wald-Wolfowitz runs test of randomness to decide whether to compress the second 8-bit image with BWIC or not at all since attempting to compress random data usually results in a larger file size. On select microarray images, by splitting a 16-bit microarray image into 8-bit pieces and selectively compressing the pieces with BWIC, we can achieve upward of 3% compression gain over our previous work.

Original languageEnglish (US)
Title of host publication2017 14th International Conference on Smart Cities
Subtitle of host publicationImproving Quality of Life Using ICT and IoT, HONET-ICT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages53-56
Number of pages4
Volume2017-January
ISBN (Electronic)9781538607596
DOIs
StatePublished - Nov 8 2017
Event14th IEEE International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017 - Irbid/Amman, Jordan
Duration: Oct 9 2017Oct 11 2017

Other

Other14th IEEE International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017
CountryJordan
CityIrbid/Amman
Period10/9/1710/11/17

Fingerprint

Microarrays
compression
Compression
Microarray
Statistical tests
statistical test
burrow
Compressors
DNA

Keywords

  • BWIC
  • DNA Microarray Images
  • Inversion Ranks
  • Lossless Compression
  • Randomness
  • Splitting bits
  • Wald-Wolfowitz Runs Test

ASJC Scopus subject areas

  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Safety Research
  • Artificial Intelligence
  • Computer Networks and Communications
  • Urban Studies

Cite this

Koc, B., Arnavut, Z., Sarkar, D., & Kocak, H. (2017). Splitting bits for lossless compression of microarray images. In 2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017 (Vol. 2017-January, pp. 53-56). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HONET.2017.8102221

Splitting bits for lossless compression of microarray images. / Koc, Basar; Arnavut, Ziya; Sarkar, Dilip; Kocak, Huseyin.

2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 53-56.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Koc, B, Arnavut, Z, Sarkar, D & Kocak, H 2017, Splitting bits for lossless compression of microarray images. in 2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 53-56, 14th IEEE International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017, Irbid/Amman, Jordan, 10/9/17. https://doi.org/10.1109/HONET.2017.8102221
Koc B, Arnavut Z, Sarkar D, Kocak H. Splitting bits for lossless compression of microarray images. In 2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 53-56 https://doi.org/10.1109/HONET.2017.8102221
Koc, Basar ; Arnavut, Ziya ; Sarkar, Dilip ; Kocak, Huseyin. / Splitting bits for lossless compression of microarray images. 2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 53-56
@inproceedings{532ec195f26c465a8fc33bf28800851e,
title = "Splitting bits for lossless compression of microarray images",
abstract = "In an earlier publication we reported on the effectiveness of the Burrows-Wheeler transformation followed by inversion coder (BWIC) in the lossless compression of DNA microarray images where we obtained gains of average 6.5{\%} over generic image compressors. In this work, we propose an enhancement of our previous technique by exploiting the bit distribution of images. Using a simple statistical test, we first decide if it will be gainful to split a 16-bit microarray image into two 8-bit images. In case of splitting, it turns out that the first 8-bit image is highly compressible and we use BWIC to compress it. The second 8-bit image most often contains noise and the bit distribution can become nearly random. We use the Wald-Wolfowitz runs test of randomness to decide whether to compress the second 8-bit image with BWIC or not at all since attempting to compress random data usually results in a larger file size. On select microarray images, by splitting a 16-bit microarray image into 8-bit pieces and selectively compressing the pieces with BWIC, we can achieve upward of 3{\%} compression gain over our previous work.",
keywords = "BWIC, DNA Microarray Images, Inversion Ranks, Lossless Compression, Randomness, Splitting bits, Wald-Wolfowitz Runs Test",
author = "Basar Koc and Ziya Arnavut and Dilip Sarkar and Huseyin Kocak",
year = "2017",
month = "11",
day = "8",
doi = "10.1109/HONET.2017.8102221",
language = "English (US)",
volume = "2017-January",
pages = "53--56",
booktitle = "2017 14th International Conference on Smart Cities",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Splitting bits for lossless compression of microarray images

AU - Koc, Basar

AU - Arnavut, Ziya

AU - Sarkar, Dilip

AU - Kocak, Huseyin

PY - 2017/11/8

Y1 - 2017/11/8

N2 - In an earlier publication we reported on the effectiveness of the Burrows-Wheeler transformation followed by inversion coder (BWIC) in the lossless compression of DNA microarray images where we obtained gains of average 6.5% over generic image compressors. In this work, we propose an enhancement of our previous technique by exploiting the bit distribution of images. Using a simple statistical test, we first decide if it will be gainful to split a 16-bit microarray image into two 8-bit images. In case of splitting, it turns out that the first 8-bit image is highly compressible and we use BWIC to compress it. The second 8-bit image most often contains noise and the bit distribution can become nearly random. We use the Wald-Wolfowitz runs test of randomness to decide whether to compress the second 8-bit image with BWIC or not at all since attempting to compress random data usually results in a larger file size. On select microarray images, by splitting a 16-bit microarray image into 8-bit pieces and selectively compressing the pieces with BWIC, we can achieve upward of 3% compression gain over our previous work.

AB - In an earlier publication we reported on the effectiveness of the Burrows-Wheeler transformation followed by inversion coder (BWIC) in the lossless compression of DNA microarray images where we obtained gains of average 6.5% over generic image compressors. In this work, we propose an enhancement of our previous technique by exploiting the bit distribution of images. Using a simple statistical test, we first decide if it will be gainful to split a 16-bit microarray image into two 8-bit images. In case of splitting, it turns out that the first 8-bit image is highly compressible and we use BWIC to compress it. The second 8-bit image most often contains noise and the bit distribution can become nearly random. We use the Wald-Wolfowitz runs test of randomness to decide whether to compress the second 8-bit image with BWIC or not at all since attempting to compress random data usually results in a larger file size. On select microarray images, by splitting a 16-bit microarray image into 8-bit pieces and selectively compressing the pieces with BWIC, we can achieve upward of 3% compression gain over our previous work.

KW - BWIC

KW - DNA Microarray Images

KW - Inversion Ranks

KW - Lossless Compression

KW - Randomness

KW - Splitting bits

KW - Wald-Wolfowitz Runs Test

UR - http://www.scopus.com/inward/record.url?scp=85043457365&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85043457365&partnerID=8YFLogxK

U2 - 10.1109/HONET.2017.8102221

DO - 10.1109/HONET.2017.8102221

M3 - Conference contribution

VL - 2017-January

SP - 53

EP - 56

BT - 2017 14th International Conference on Smart Cities

PB - Institute of Electrical and Electronics Engineers Inc.

ER -