Local Regression based Colorization Coding

Paul Oh, Suk Ho Lee, Moon Gi Kang

2014

Abstract

A new image coding technique for color image based on colorization method is proposed. In colorization based image coding, the encoder selects the colorization coefficients according to the basis made from the luminance channel. Then, in the decoder, the chrominance channels are reconstructed by utilizing the luminance channel and the colorization coefficients sent from the encoder. The main issue in colorization based coding is to extract colorization coefficients well such that the compression rate and the quality of the reconstructed color becomes good enough. In this paper, we use a local regression method to extract the correlated feature between the luminance channel and the chrominance channels. The local regions are obtained by performing an image segmentation on the luminance channel both in the encoder and the decoder. Then, in the decoder, the chrominance values in each local region are reconstructed via a local regression method. The use of the correlated features helps to colorize the image with more details. The experimental results show that the proposed algorithm performs better than JPEG and JPEG2000 in terms of the compression rate and the PSNR value.

References

  1. Cheng, L., and Vishwanathan, S. V. N., (2007). Learning to compress images and videos. In Proc. Int. Conf. Mach. Learn., vol. 227., pp. 161-168.
  2. Ono, S., Miyata, T., and Sakai, Y., (2010). Colorizationbased coding by focusing on characteristics of colorization bases. In Proc. Picture Coding Symp., pp. 230-233
  3. Lee, S., Park, S.W., Oh, P., Kang, M. G., (2013). Colorization-based compression using optimization. IEEE Trans. Image Processing, vol. 22. No. 7, pp. 2627-2636
  4. Levin, A., Lischinski, D., and Weiss, Y., (2004). Colorization using optimization. ACM Trans. Graph., vol. 23, no. 3, pp. 689-694.
  5. Chen, S. S., Donoho, D. L., and Saunders, M. A., (1998). Atomic decomposition by basis pursuit. SIAM J. Sci. Comput., vol. 20, no. 1, pp. 33-61.
  6. Tropp, J. A., and Gilbert, A. C., (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666.
  7. Candés, E., and Tao, T., (2005). Decoding by linear programing. IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215..
  8. Candés, E., and Tao, T., (2006). Near optimal signal recovery from random projections: Universal encoding strategies. IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406-5425..
  9. Donoho, D., (2006). Compressed sensing. IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306.
  10. Comaniciu, D., and Meer, P., (2002). Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603- 619.
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Paper Citation


in Harvard Style

Oh P., Lee S. and Kang M. (2014). Local Regression based Colorization Coding . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 153-159. DOI: 10.5220/0004728401530159


in Bibtex Style

@conference{visapp14,
author={Paul Oh and Suk Ho Lee and Moon Gi Kang},
title={Local Regression based Colorization Coding},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={153-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004728401530159},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Local Regression based Colorization Coding
SN - 978-989-758-003-1
AU - Oh P.
AU - Lee S.
AU - Kang M.
PY - 2014
SP - 153
EP - 159
DO - 10.5220/0004728401530159