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Image Compression Based on Visual Saliency at Individual Scales

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

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Abstract

The goal of lossy image compression ought to be reducing entropy while preserving the perceptual quality of the image. Using gaze-tracked change detection experiments, we discover that human vision attends to one scale at a time. This evidence suggests that saliency should be treated on a per-scale basis, rather than aggregated into a single 2D map over all the scales. We develop a compression algorithm which adaptively reduces the entropy of the image according to its saliency map within each scale, using the Laplacian pyramid as both the multiscale decomposition and the saliency measure of the image. We finally return to psychophysics to evaluate our results. Surprisingly, images compressed using our method are sometimes judged to be better than the originals.

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References

  1. Burt, P., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Communication 31 (1983)

    Google Scholar 

  2. Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. PAMI (1989)

    Google Scholar 

  3. Simoncelli, E., Adelson, E.: Noise removal via Bayesian wavelet coring. In: IEEE International Conference on Image Processing, vol. 1 (1996)

    Google Scholar 

  4. Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Information Theory 4, 613–627 (1995)

    Article  MathSciNet  Google Scholar 

  5. Chambolle, A., DeVore, R.A., Lee, N., Lucier, B.J.: Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans. Image Processing 7, 319–335 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  6. DeVore, R., Jawerth, B., Lucier, B.: Image compression through wavelet transform coding. IEEE Transactions on Information Theory 32 (1992)

    Google Scholar 

  7. Golner, M.A., Mikhael, W.B., Krishnang, V.: Modified jpeg image compression with region-dependent quantization. Circuits, Systems, and Signal Processing 21, 163–180 (2002)

    Article  MATH  Google Scholar 

  8. Lee, S.-H., Shin, J.-K., Lee, M.: Non-uniform image compression using biologically motivated saliency map model. In: Intelligent Sensors, Sensor Networks and Information Processing Conference (2004)

    Google Scholar 

  9. Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. Image Processing 13, 669–673 (2003)

    Google Scholar 

  10. Itti, L., Koch, C.: Computational modelling of visual attention. Nature Neuroscience, 194–203 (2001)

    Google Scholar 

  11. Torralba, A.: Contextual influences on saliency. In: Itti, L., Rees, G., Tsotsos, J. (eds.) Neurobiology of Attention, pp. 586–593. Academic Press, London (2004)

    Google Scholar 

  12. Rensink, R.A.: Change detection. Annual Review of Psychology 53, 4245–4277 (2002)

    Article  Google Scholar 

  13. O’Regan, J.K., Deubel, H., Clark, J.J., Rensink, R.A.: Picture changes during blinks: looking without seeing and seeing without looking. Visual Cognition 7, 191–211 (2000)

    Article  Google Scholar 

  14. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: International Conference on Computer Vision (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Boston College, Chestnut Hill, MA, 02467, USA

    Stella X. Yu

  2. VideoIQ, Inc., Bedford, MA, 01730, USA

    Dimitri A. Lisin

Authors
  1. Stella X. Yu
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  2. Dimitri A. Lisin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Yu, S.X., Lisin, D.A. (2009). Image Compression Based on Visual Saliency at Individual Scales. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_15

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  • DOI: https://doi.org/10.1007/978-3-642-10331-5_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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