ABSTRACT
We present an intuitive scheme for lossy color-image compression: Use the color information from a few representative pixels to learn a model which predicts color on the rest of the pixels. Now, storing the representative pixels and the image in grayscale suffice to recover the original image. A similar scheme is also applicable for compressing videos, where a single model can be used to predict color on many consecutive frames, leading to better compression. Existing algorithms for colorization -- the process of adding color to a grayscale image or video sequence -- are tedious, and require intensive human-intervention. We bypass these limitations by using a graph-based inductive semi-supervised learning module for colorization, and a simple active learning strategy to choose the representative pixels. Experiments on a wide variety of images and video sequences demonstrate the efficacy of our algorithm.
- Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373--1396. Google ScholarDigital Library
- Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res., 7, 2399--2434. Google ScholarDigital Library
- Chapelle, O., Schölkopf, B., & Zien, A., eds. (2006). Semi-Supervised Learning. Cambridge, MA: MIT Press.Google Scholar
- Levin, A., Lischinski, D., & Weiss, Y. (2004). Colorization using optimization. In SIGGRAPH '04: ACM SIGGRAPH 2004 Papers, 689--694. New York, NY, USA: ACM Press. Google ScholarDigital Library
- Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. In Proc. 9th Int'l. Conf. Computer Vision, vol. 1, 10--17. Google ScholarDigital Library
- Schölkopf, B., & Smola, A. (2002). Learning with Kernels. Cambridge, MA: MIT Press.Google Scholar
- Smola, A. J., & Kondor, I. R. (2003). Kernels and regularization on graphs. In B. Schöölkopf, & M. K. Warmuth, eds., Proc. Annual Conf. Computational Learning Theory, Lecture Notes in Comput. Sci., 144--158. Heidelberg, Germany: Springer-Verlag.Google Scholar
- Zhang, T., & Ando, R. K. (2005). Graph based semisupervised learning and spectral kernel design. Tech. Rep. RC23713, IBM T.J. Watson Research Center.Google Scholar
- Zhu, X. (2005). Semi-supervised learning literature survey. Tech. Rep. 1530, Computer Sciences, University of Wisconsin-Madison. Http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf.Google Scholar
- Learning to compress images and videos
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