Compression Impact on LIRE-based CBIR of Colonoscopy Data

Conference paper
Part of the Informatik aktuell book series (INFORMAT)


In a large experimental study, the impact of lossy image compression standards on LIRE-based CBIR in different compression scenarios is assessed. Image retrieval is conducted using NBI-colonoscopic imagery with the aim of polyp dignity assessment. Results clearly indicate that (1) JPEG2000 compression does hardly impact retrieval results, (2) it is important to compress both query and retrieval data in case of JPEG and JPEG XR, and (3) some LIRE desciptors deliver good retrieval results on these data which calls for further investigations.


Compression Ratio Image Retrieval Local Binary Pattern Query Image Retrieval Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rabenstein T, et al. Tele-endoscopy: influence of data compression, bandwidth and simulated impairments on the usability of real-time digital video endoscopy transmissions for medical diagnoses. Endoscopy. 2002;34(9):703–10.CrossRefGoogle Scholar
  2. 2.
    Lux M. LIRE: open source image retrieval in java. Proc ACM Int Conf Multimedia. 2013; p. 843–6.Google Scholar
  3. 3.
    Chao J, Al-Nuaimi A, Schroth G, et al. Performance comparison of various feature detector-descriptor combinations for content-based image retrieval with JPEGencoded query images. Proc IEEE Workshop Multimedia Signal Process (MMSP). 2013.Google Scholar
  4. 4.
    Tamaki T, et al. Computer-aided colorectal tumor classification in NBI endoscopy using local features. Med Image Anal. 2013;17(1):78–100.CrossRefGoogle Scholar
  5. 5.
    Häfner M, Brunauer L, Payer H, et al. Computer-aided classification of zoomendoscopical images using fourier filters. IEEE Trans Inf Technol Biomed. 2010;14(4):958–70.CrossRefGoogle Scholar
  6. 6.
    Häfner M, Liedlgruber M, Uhl A, et al. Color treatment in endoscopic image classification using multi-scale local color vector patterns. Med Image Anal. 2012;16(1):75–86.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer SciencesUniversity of SalzburgAustria
  2. 2.St. Elisabeth HospitalViennaAustria
  3. 3.Department of Information EngineeringHiroshima UniversityJapan
  4. 4.Hiroshima University HospitalHiroshimaJapan
  5. 5.Hiroshima General Hospital of West Japan Railway CompanyJapan

Personalised recommendations