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A unified active and semi-supervised learning framework for image compression
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
We consider the problem of lossy image compression from machine learning perspective. Typical image compression algorithms first transform the image from its spatial domain representation to frequency domain representation using some transform technique, such as Discrete Cosine Transform and Discrete Wavelet Transform, and then code the transformed values. Recently, instead of performing a frequency transformation, machine learning based approach has been proposed which uses the color
doi:10.1109/cvprw.2009.5206835
fatcat:swsobf4xurdndes22icfoq5ave