DWT based Low Power Image Compressor for Wireless Capsule Endoscopy

Kushaagra Goyal, Abhishek Lal, Basabi Bhaumik

Abstract

In WCE literature so far, the stress is on having an image compressor with low power consumption and silicon area. However one needs to consider the image compressor along with the serialiser, the interface between image compressor and transmitter as a single unit. In this paper, we propose the design of a hardware efficient, low power image compression system along with the serialiser for wireless capsule endoscopy. It is based on integer version of discrete wavelet transform and uses low complexity encoders like adaptive Golomb-Rice encoder. An alternative architecture for serialiser is proposed specific to the algorithm which runs at only 8 times instead of 32 times the frequency required at the existing compressors in the literature. The proposed algorithm gives a compression of 91.88 percent at a PSNR of 38.17. The implementation of the compressor plus serialiser in 130nm HS (high speed) standard CMOS process technology consumes 16.9uW of power at 2 frames per second for 256256 image. Compared to the existing designs at similar power consumption, the proposed scheme reduces the serialiser’s frequency by a factor of four besides giving at least 1.5 % higher compression.

References

  1. Bhanu, U. and Chilambuchelvan, D. A. (2012). A detailed suvey on vlsi architectures for lifting based dwt for efficient hardware implementation. International Journal of VLSI design and Communication Systems, pages 207-214.
  2. Bruaene, C. V. D., Looze, D. D., and Hindryckx, P. (2015). Small bowel capsule endoscopy: Where are we after 15 years of use? World Journal of Gastrointestinal Endoscopy, pages 13-36.
  3. Cosman, P. C., Gray, R. M., and Olshen, R. A. (1994). Evaluating quality of compressed medical images: Snr, subjective rating, and diagnostic accuracy. In Proceedings of the IEEE, volume 82, pages 919-932.
  4. Fante, K. A., Bhaumik, B., and Chatterjee, S. (2016). Design and implementation of computationally efficient image compressor for wireless capsule endoscopy. Circuits Systems and Signal Processing, 35:1677- 1703.
  5. Gastrolab (2014). http://www.gastrolab.net.
  6. Hale, M. F., Sidhu, R., and McAlindon, M. E. (2014). Capsule endoscopy: Current practice and future directions. World Journal of Gastroenterology, pages 7752-7759.
  7. Jing, Z., Jin-yun, F., and Cheng-de, H. (2008). The selection of reversible integer-to-integer wavelet transforms for dem multi-scale representation and progressive compression. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, pages 1010-1024.
  8. Khan, T. H. and Wahid, K. (2011a). Lossless and low power image compressor for wireless capsule endoscopy. VLSI Design.
  9. Khan, T. H. and Wahid, K. (2011b). Low power and low complexity compressor for video capsule endoscopy. In IEEE Transactions on Circuits And Systems For Video Technology, volume 21, page 15341546.
  10. Khan, T. H. and Wahid, K. (2012). Implantable narrow band image compressor for capsule endoscopy. In IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, South Korea, page 22032206.
  11. Khan, T. H. and Wahid, K. (2013). Subsample based image compression for capsule endoscopy. Real-Time Image Processing, pages 5-19.
  12. Korhonen, J. and Junyong, Y. (2012). Peak signal to noise ratio revisited: Is simple beautiful? Fourth International Workshop on Quality of Multimedia Experience (QoMEX), Yarra Valley, VIC, Australia, pages 37-38.
  13. Koulaouzidis, A. and Iakovidis, D. K. (2015). Wireless endoscopy in 2020: Will it still be a capsule? World Journal of Gastroenterology, pages 5119-5130.
  14. Lin, M.-Ch, D. L.-R. and Weng, P, K. (2006). An ultralow-power image compressor for capsule endoscope. Biomedical Engineering Online, pages 1-8.
  15. Memon, N. (1998). Adaptive coding of dct coefficients by golomb-rice codes. In Proceedings of International Conference on Image Processing.
  16. Philip, N., Martini, M. G., and Amso, N. (2008). Subjective and objective quality assessment in wireless tele ultrasonography imaging. In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5346-5349.
  17. Turcza, P. and Duplaga, M. (2011). Low power fpga-based image processing core for wireless capsule endoscopy. Sensors and Actuators A: Physical, pages 552-560.
  18. Turcza, P. and Duplaga, M. (2013). Hardware-efficient lowpower image processing system for wireless capsule endoscopy. IEEE Journal Of Biomedical And Health Informatics, pages 1046-1056.
Download


Paper Citation


in Harvard Style

Goyal K., Lal A. and Bhaumik B. (2017). DWT based Low Power Image Compressor for Wireless Capsule Endoscopy . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES, (BIOSTEC 2017) ISBN 978-989-758-216-5, pages 17-24. DOI: 10.5220/0006103000170024


in Bibtex Style

@conference{biodevices17,
author={Kushaagra Goyal and Abhishek Lal and Basabi Bhaumik},
title={DWT based Low Power Image Compressor for Wireless Capsule Endoscopy},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES, (BIOSTEC 2017)},
year={2017},
pages={17-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006103000170024},
isbn={978-989-758-216-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES, (BIOSTEC 2017)
TI - DWT based Low Power Image Compressor for Wireless Capsule Endoscopy
SN - 978-989-758-216-5
AU - Goyal K.
AU - Lal A.
AU - Bhaumik B.
PY - 2017
SP - 17
EP - 24
DO - 10.5220/0006103000170024