QPAC: A Novel Document Image Compression Technique Based on ANFIS for Calibrated Quality Preservation

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 221)

Abstract

In the present paper, a novel compression technique is used to compress the document images without compromising abrupt quality degradation. An Adaptive neuro-fuzzy inference system (ANFIS) based classification scheme is used for segmenting the input document followed by some neighborhood smoothing is performed. A tuning based adaptive compression scheme is used for compressing the ANFIS classified data. A 3D trade-off between quality, compression ratio and relative occupancy of image region is addressed in a calibrated manner.

Keywords

Adaptive neuro-fuzzy inference system Segmentation Classification Decision tree Compression PSNR 

References

  1. 1.
    Chuai-aree S, Lursinsap C, Sophatsathit P, Siripant S (2001) Fuzzy c-mean: a statistical feature classification of text and image segmentation method. In: Proceedings of International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, ACM, vol 9, pp 661–671, Nov 2001 Google Scholar
  2. 2.
    Shadkami P, Bonnier N (2010) Watershed Based Document Image Analysis. In: Proceedings of 12th International Conference, ACIVS, LNCS, vol 1, pp 114–124. Springer, Sydney, 13–16 Dec 2010 Google Scholar
  3. 3.
    Lin M, Tapamo J, Ndovie B (2006) A texture-based method for document segmentationand classification. Jt Spec Issue Adv End User Data Min Tech 36:49–56Google Scholar
  4. 4.
    Bottou L, Haffner P, Howard P, Simard P, Bengio Y, LeCun Y (1998) high quality document image compression with DjVu. J Electron Imaging 7(3):410–425CrossRefGoogle Scholar
  5. 5.
    Das A, Remya R (2012) A novel scheme of orientation and scale mapped RDC(OS-RDC) to improve compression in document images ensuring quality preservation. In: International Conference on Pattern Recognition, Tsukuba, Japan 2012Google Scholar
  6. 6.
    Das A, Parua S (2012) Psycho-visual evaluation of contrast enhancement algorithms by adaptive neuro-fuzzy inference system. Lect notes comput sci, springer 7143:75–83Google Scholar
  7. 7.
    Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, USA Google Scholar
  8. 8.
  9. 9.
    Jang JSR (1992) Self learning fuzzy controllers basedon temporal back propagation. IEEE trans Neural Netw 3(5):714–723Google Scholar
  10. 10.
    Jang JSR (1993) ANFIS: Adaptive neuro-fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–683Google Scholar
  11. 11.
    Koutchev R, Milanova M, Todorov V, Koutchev R (2006) Document image compression with IDP and adaptive RLE. In: Proceedings of 32nd Annual conference on IEEE Industrial Electronics, IEEE pp 2361–2366. Paris, 6–10 Nov 2006Google Scholar
  12. 12.
    Imura H, Tanaka Y (2009) Compression and string matching algorithm for printed document images. In: Proceedings of 10th International Conference on Document Analysis and recognition. pp 291–295, 26–29 July 2009Google Scholar
  13. 13.
    Wallace GK (1992) The JPEG still picture compression standard. IEEE Transa Consum Electron 38(1) (Feb)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Imaging Tech LabHCL Technologies LtdChennaiIndia

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