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Textual image compression at low bit rates based on region-of-interest coding

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Abstract

In this paper, we deal with those applications of textual image compression where high compression ratio and maintaining or improving the visual quality and readability of the compressed images are of main concern. In textual images, most of the information exists in the edge regions; therefore, the compression problem can be studied in the framework of region-of-interest (ROI) coding. In this paper, the Set Partitioning in Hierarchical Trees (SPIHT) coder is used in the framework of ROI coding along with some image enhancement techniques in order to remove the leakage effect which occurs in the wavelet-based low-bit-rate compression. We evaluated the compression performance of the proposed method with respect to some qualitative and quantitative measures. The qualitative measures include the averaged mean opinion scores (MOS) curve along with demonstrating some outputs in different conditions. The quantitative measures include two proposed modified PSNR measures and the conventional one. Comparing the results of the proposed method with those of three conventional approaches, DjVu, JPEG2000, and SPIHT coding, showed that the proposed compression method considerably outperformed the others especially from the qualitative aspect. The proposed method improved the MOS by 20 and 30 %, in average, for high- and low-contrast textual images, respectively. In terms of the modified and conventional PSNR measures, the proposed method outperformed DjVu and JPEG2000 up to 0.4 dB for high-contrast textual images at low bit rates. In addition, compressing the high contrast images using the proposed ROI technique, compared to without using this technique, improved the average textual PSNR measure up to 0.5 dB, at low bit rates.

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References

  1. Grailu, H., Lotfizad, M., Sadoghi Yazdi, H.: Farsi and Arabic document images lossy compression based on the mixed raster content model. Int. J. Doc. Anal. Recogn. (IJDAR) 12(4), 227–248 (2009)

    Article  Google Scholar 

  2. Said, A., Pearlman, W.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6(3), 243–250 (1996)

    Article  Google Scholar 

  3. Pearlman, W.A., Said, A.: Digital Signal Compression: Principles and Practice. Cambridge University Press, New York (2011)

    Book  Google Scholar 

  4. Simard, P.Y., Malvar, H.S.: A wavelet coder for masked images. In: Data Compression Conference (DCC), pp. 93–102 (2001)

  5. Ghanbari, M.: Standard Codecs: Image Compression to Advanced Video Coding, 3rd edn. The Institution of Engineering and Technology, London (2011)

    Book  Google Scholar 

  6. Mahesh, P., Rajesh, P., Suneetha, I.: Improved block-based segmentation for JPEG compressed document images. Int. J. Res. Eng. Technol. 2(11), 669–673 (2013)

    Article  Google Scholar 

  7. Oztan, B., Malik, A., Fan, Z., Eschbach, R.: Removal of artifacts from JPEG compressed document images. In: Proceedings of the SPIE, Color Imaging XII: Processing, Hardcopy, and Applications, vol. 6493, pp. 649306-1–649306-9 (2007)

  8. Taubman, D.: High performance scalable image compression with EBCOT. IEEE Trans. Image Process. 9(7), 1151–1170 (2000)

    Google Scholar 

  9. Zaghetto, A., de Queiroz, R.L.: Scanned document compression using a block-based hybrid video codec. IEEE Trans. Image Process. 22(6), 2420–2428 (2013)

    Article  MathSciNet  Google Scholar 

  10. Feng, G., Bouman, C.A., Cheng, H.: High quality MRC document coding. IEEE Trans. Image Process. 15(10), 3152–3169 (2006)

    Article  Google Scholar 

  11. de Queiroz, R.L., Buckley, R.R., Xu, M.: Mixed raster content (MRC) model for compound image compression. Vis. Commun. Image Process. 3653, 1106–1117 (1998)

    Article  Google Scholar 

  12. Lam, E.Y.: Compound document compression with model-based biased reconstruction. J. Electron. Imaging 13(1), 191–197 (2004)

    Article  Google Scholar 

  13. Khangar, S.V., Malik, L.G.: Handwritten text image compression for indic script document. Int. J. Comput. Appl. 47(5), 11–16 (2012)

    Google Scholar 

  14. Feng, G., Bouman, C.A.: High-quality MRC document coding. IEEE Trans. Image Process. 15(10), 3152–3169 (2006)

    Article  Google Scholar 

  15. de Queiroz, R.L., Buckley, R.R., Xu, M.: Mixed raster content (MRC) model for compound image compression. Proc. SPIE Vis. Commun. Image Process. 3653, 1106–1117 (1999)

    Article  Google Scholar 

  16. Fan, Z., Jacobs, T.: Segmentation for mixed raster contents with multiple extracted constant color areas. Proc. SPIE Color Imaging X: Process. Hardcopy Appl. 5667, 251–262 (2005)

    Google Scholar 

  17. Huttenlocher, D.P., Felzenswalb, P.F., Rucklidge, W.: DigiPaper: a versatile color document image representation. In: International Conference on Image Processing (ICIP), pp. 219–223 (1999)

  18. Grailu, H., Lotfizad, M., Sadoghi Yazdi, H.: 1-D chaincode pattern matching for compression of bi-level printed Farsi and Arabic textual images. Image Vis. Comput. 27(10), 1615–1625 (2009)

    Article  Google Scholar 

  19. Grailu, H., Lotfizad, M., Sadoghi Yazdi, H.: An improved pattern matching technique for lossy/lossless compression of binary printed Farsi and Arabic textual images. Int. J. Intell. Comput. Cybern. 2(1), 120–147 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. Grailu, H., Lotfizad, M., Sadoghi Yazdi, H.: A lossy/lossless compression method for printed typeset bi-level text images based on improved pattern matching. Int. J. Doc. Anal. Recogn. 11(4), 159–182 (2009)

  21. Awajan, A.: Multilayer model for Arabic text compression. Int. Arab J. Inf. Technol. 8(2), 188–196 (2011)

    Google Scholar 

  22. Bottou, L., Haffner, P., Howard, P.G., Simard, P., Bengio, Y., LeCun, Y.: High quality document image compression with DjVu. J. Electron. Imaging 7(3), 410–425 (1998)

    Article  Google Scholar 

  23. Barthel, K.-U., Partlin, S.M., Thierschmann, M.: New technology for raster document image compression. Proc. SPIE Doc. Recogn. Retr. VII 3967, 286–290 (2000)

    Article  Google Scholar 

  24. Thierschmann, M., Barthel, K.-U., Martin, U.-E.: A scalable DSP-architecture for high-speed color document compression. Proc. SPIE Doc. Recogn. Retr. VIII 4307, 158–166 (2001)

    Article  Google Scholar 

  25. Wu, B.-F., Chiu, C.-C., Chen, Y.-L.: Algorithms for compressing compound document images with large text/background overlap. IEE Proc. Vis. Image Signal Process. 151(6), 453–459 (2004)

    Article  Google Scholar 

  26. Haneda, E., Yi, J., Bouman, C.A.: Segmentation for MRC compression. Process. SPIE Color Imaging XII: Process. Hardcopy Appl. 6493, 252–262 (2007)

    Google Scholar 

  27. http://www.mathworks.com, Image Processing Toolbox, See Documentation for “Imadjust” Function

  28. http://www.mathworks.com, Image Processing Toolbox, See Documentation for “Imsharpen” Function

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Acknowledgments

I acknowledge the reviewers as well as the Associate Editor for their valuable comments. This work was supported by the University of Shahrood under the Grant Number 13039.

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Correspondence to Hadi Grailu.

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This work was supported by the University of Shahrood under the Grant Number 13039.

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Grailu, H. Textual image compression at low bit rates based on region-of-interest coding. IJDAR 19, 65–81 (2016). https://doi.org/10.1007/s10032-015-0258-7

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