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A Subsample-Based Low-Power Image Compressor for Capsule Gastrointestinal Endoscopy

Abstract

In the design of capsule endoscope, the trade-offs between battery-life and video-quality is imperative. Typically, the resolution of capsule gastrointestinal (GI) image is limited for the power consumption and bandwidth of RF transmitter. Many fast compression algorithms for reducing computation load; however, they may result in a distortion of the original image, which is not suitable for the use of medical care. This paper presents a novel image compression for capsule gastrointestinal endoscopy, called GICam-II, motivated by the reddish feature of GI image. The reddish feature makes the luminance or sharpness of GI image sensitive to the red component as well as the green component. We focus on a series of mathematical statistics to systematically analyze the color sensitivity in GI images from the RGB color space domain to the two-dimensional discrete-cosine-transform spatial frequency domain. To reduce the compressed image size, GICam-II downsamples the blue component without essential loss of image detail and also subsamples the green component from the Bayer-patterned image. From experimental results, the GICam-II can significantly save the power consumption by 38.5% when compared with previous one and 98.95% when compared with JPEG compression, while the average peak signal-to-noise ratio of luminance (PSNRY) is 40.73 dB.

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Correspondence to Meng-Chun Lin.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lin, MC., Dung, LR. A Subsample-Based Low-Power Image Compressor for Capsule Gastrointestinal Endoscopy. EURASIP J. Adv. Signal Process. 2011, 257095 (2011). https://doi.org/10.1155/2011/257095

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  • DOI: https://doi.org/10.1155/2011/257095

Keywords

  • Power Consumption
  • Image Compression
  • Compression Algorithm
  • Space Domain
  • JPEG Compression