Compressing Images with Diffusion- and Exemplar-Based Inpainting

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)

Abstract

Diffusion-based image compression methods can surpass state-of-the-art transform coders like JPEG 2000 for cartoon-like images. However, they are not well-suited for highly textured image content. Recently, advances in exemplar-based inpainting have made it possible to reconstruct images with non-local methods from sparse known data. In our work we compare the performance of such exemplar-based and diffusion-based inpainting algorithms, dependent on the type of image content. We use our insights to construct a hybrid compression codec that combines the strengths of both approaches. Experiments demonstrate that our novel method offers significant advantages over state-of-the-art diffusion-based methods on textured image data and can compete with transform coders.

Keywords

Exemplar-based inpainting Diffusion-based inpainting Image compression Texture 

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References

  1. 1.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  2. 2.
    Arias, P., Facciolo, G., Caselles, V., Sapiro, G.: A variational framework for exemplar-based image inpainting. International Journal of Computer Vision 93(3), 319–347 (2011)CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. IEEE Transactions on Image Processing 12(8), 882–889 (2003)CrossRefGoogle Scholar
  4. 4.
    Cao, F., Gousseau, Y., Masnou, S., Pérez, P.: Geometrically guided exemplar-based inpainting. SIAM Journal on Imaging Sciences 4(4), 1143–1179 (2011)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Charbonnier, P., Blanc-Féraud, L., Aubert, G., Barlaud, M.: Deterministic edge-preserving regularization in computed imaging. IEEE Transactions on Image Processing 6(2), 298–311 (1997)CrossRefGoogle Scholar
  6. 6.
    Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Transactions on Image Processing 13(9), 1200–1212 (2004)CrossRefGoogle Scholar
  7. 7.
    Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: Proc. Seventh IEEE International Conference on Computer Vision, Corfu, vol. 2, pp. 1033–1038, September 1999Google Scholar
  8. 8.
    Facciolo, G., Arias, P., Caselles, V., Sapiro, G.: Exemplar-based interpolation of sparsely sampled images. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 331–344. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  9. 9.
    Galić, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., Seidel, H.-P.: Towards PDE-based image compression. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds.) VLSM 2005. LNCS, vol. 3752, pp. 37–48. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  10. 10.
    Gautier, J., Meur, O.L., Guillemot, C.: Efficient depth map compression based on lossless edge coding and diffusion. In: Proc. 29th Picture Coding Symposium, Krakow, Poland, pp. 81–84, May 2012Google Scholar
  11. 11.
    Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Transactions on Graphics 26(3), 4 (2007)Google Scholar
  12. 12.
    Hoffmann, S., Mainberger, M., Weickert, J., Puhl, M.: Compression of depth maps with segment-based homogeneous diffusion. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds.) SSVM 2013. LNCS, vol. 7893, pp. 319–330. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Li, Y., Sjostrom, M., Jennehag, U., Olsson, R.: A scalable coding approach for high quality depth image compression. In: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, Zurich, Switzerland, pp. 1–4, October 2012Google Scholar
  14. 14.
    Liu, D., Sun, X., Wu, F., Li, S., Zhang, Y.Q.: Image compression with edge-based inpainting. IEEE Transactions on Circuits, Systems and Video Technology 17(10), 1273–1286 (2007)CrossRefGoogle Scholar
  15. 15.
    Mahoney, M.: Adaptive weighing of context models for lossless data compression. Tech. Rep. CS-2005-16, Florida Institute of Technology, Melbourne, Florida, December 2005Google Scholar
  16. 16.
    Mainberger, M., Bruhn, A., Weickert, J., Forchhammer, S.: Edge-based compression of cartoon-like images with homogeneous diffusion. Pattern Recognition 44(9), 1859–1873 (2011)CrossRefGoogle Scholar
  17. 17.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. Eigth International Conference on Computer Vision, Vancouver, Canada, pp. 416–423, July 2001Google Scholar
  18. 18.
    Pennebaker, W.B., Mitchell, J.L.: JPEG: Still Image Data Compression Standard. Springer, New York (1992)Google Scholar
  19. 19.
    Peter, P.: Three-dimensional data compression with anisotropic diffusion. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 231–236. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  20. 20.
    Peter, P., Weickert, J.: Colour image compression with anisotropic diffusion. In: Proc. 21st IEEE International Conference on Image Processing, Paris, France, October 2014 (in press)Google Scholar
  21. 21.
    Rane, S.D., Sapiro, G., Bertalmio, M.: Structure and texture fillingin of missing image blocks in wireless transmission and compression applications. IEEE Transactions on Image Processing 12(3), 296–302 (2003)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Schmaltz, C., Peter, P., Mainberger, M., Ebel, F., Weickert, J., Bruhn, A.: Understanding, optimising, and extending data compression with anisotropic diffusion. International Journal of Computer Vision 108(3), 222–240 (2014)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Schmaltz, C., Weickert, J., Bruhn, A.: Beating the quality of JPEG 2000 with anisotropic diffusion. In: Denzler, J., Notni, G., Süße, H. (eds.) Pattern Recognition. LNCS, vol. 5748, pp. 452–461. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  24. 24.
    Sun, J., Yuan, L., Jia, J., Shum, H.Y.: Image completion with structure propagation. ACM Transactions on Graphics 24(3), 861–868 (2005)CrossRefGoogle Scholar
  25. 25.
    Taubman, D.S., Marcellin, M.W. (eds.): JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer, Boston (2002) Google Scholar
  26. 26.
    Weickert, J.: Theoretical foundations of anisotropic diffusion in image processing. Computing Supplement, vol. 11, pp. 221–236 (1996)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany

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