Skip to content

Advertisement

Open Access

A Near-Lossless Image Compression Algorithm Suitable for Hardware Design in Wireless Endoscopy System

EURASIP Journal on Advances in Signal Processing20062007:082160

https://doi.org/10.1155/2007/82160

Received: 12 September 2005

Accepted: 7 April 2006

Published: 24 September 2006

Abstract

In order to decrease the communication bandwidth and save the transmitting power in the wireless endoscopy capsule, this paper presents a new near-lossless image compression algorithm based on the Bayer format image suitable for hardware design. This algorithm can provide low average compression rate ( bits/pixel) with high image quality (larger than dB) for endoscopic images. Especially, it has low complexity hardware overhead (only two line buffers) and supports real-time compressing. In addition, the algorithm can provide lossless compression for the region of interest (ROI) and high-quality compression for other regions. The ROI can be selected arbitrarily by varying ROI parameters. In addition, the VLSI architecture of this compression algorithm is also given out. Its hardware design has been implemented in m CMOS process.

Keywords

Information TechnologyQuantum InformationImage CompressionCompression AlgorithmHardware Design

[1234567891011121314]

Authors’ Affiliations

(1)
Department of Electronic Engineering, Tsinghua University, Beijing, China

References

  1. Iddan G, Meron G, Glukhovsky A, Swain P: Wireless capsule endoscopy. Nature 2000,405(6785):417-418.View ArticleGoogle Scholar
  2. Xie X, Li G, Chen X, et al.: A novel low power IC design for bi-directional digital wireless endoscopy capsule system. Proceedings of IEEE International Workshop on Biomedical Circuits and Systems (BIoCAS '04), December 2004, Singapore, Republic of Singapore S1.8-5-S1.8-8 .Google Scholar
  3. Tsai YTi: Color image compression for single-chip cameras. IEEE Transactions on Electron Devices 1991,38(5):1226-1232. 10.1109/16.78401View ArticleGoogle Scholar
  4. Toi T, Ohita M: A subband coding technique for image compression in single CCD cameras with Bayer color filter arrays. IEEE Transactions on Consumer Electronics 1999,45(1):176-180. 10.1109/30.754434View ArticleGoogle Scholar
  5. Lee S-Y, Ortega A: A novel approach of image compression in digital cameras with a Bayer color filter array. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 482-485.Google Scholar
  6. Koh CC, Mukherjee J, Mitra SK: New efficient methods of image compression in digital cameras with color filter array. IEEE Transactions on Consumer Electronics 2003,49(4):1448-1456. 10.1109/TCE.2003.1261253View ArticleGoogle Scholar
  7. Battiato S, Buemi A, Della Torre L, Vitali A: A fast vector quantization engine for CFA data compression. Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03), June 2003, Grado, ItalyGoogle Scholar
  8. Zhang N, Wu X: Lossless compression of color mosaic images. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore, Republic of Singapore 1: 517-520.Google Scholar
  9. Bayer BE: Color imaging array. US patent no. 3,971,065, 1976Google Scholar
  10. Weinberger MJ, Seroussi G, Sapiro G: The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Transactions on Image Processing 2000,9(8):1309-1324. 10.1109/83.855427View ArticleGoogle Scholar
  11. Wu X: Lossless compression of continuous-tone images via context selection, quantization, and modeling. IEEE Transactions on Image Processing 1997,6(5):656-664. 10.1109/83.568923View ArticleGoogle Scholar
  12. Howard PG, Vitter JS: Fast and efficient lossless image compression. Proceedings of the IEEE Data Compression Conference (DCC '93), April 1993, Snowbird, Utah, USA 351-360.Google Scholar
  13. Xie X, Li G, Zhang C, Wang Z: An efficient control strategy of adaptive packet length for ARQ in wireless endoscopy system. Proceedings of International Symposium on Communications and Information Technologies (ISCIT '05), October 2005, Beijing, China 2: 1121-1123.Google Scholar
  14. Golomb SW: Run-length encodings. IEEE Transactions on Information Theory 1966,12(3):399-401. 10.1109/TIT.1966.1053907MathSciNetView ArticleMATHGoogle Scholar

Copyright

© Xie et al. 2007

Advertisement