Learning to compress images and videos

Li Cheng*, S. V.N. Vishwanathan

*Corresponding author for this work

    Research output: Contribution to conferencePaperpeer-review

    57 Citations (Scopus)

    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.

    Original languageEnglish
    Pages161-168
    Number of pages8
    DOIs
    Publication statusPublished - 2007
    Event24th International Conference on Machine Learning, ICML 2007 - Corvalis, OR, United States
    Duration: 20 Jun 200724 Jun 2007

    Conference

    Conference24th International Conference on Machine Learning, ICML 2007
    Country/TerritoryUnited States
    CityCorvalis, OR
    Period20/06/0724/06/07

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