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Learning to Compress Images and Video

Learning to Compress Images and Video

This video was recorded at 24th Annual International Conference on Machine Learning (ICML), Corvallis 2007. 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.

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