Learning to Compress Images and Video

author: Li Cheng, NICT - National Institute of Information and Communications Technology
published: June 23, 2007,   recorded: June 2007,   views: 8869


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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.

See Also:

Download slides icon Download slides: icml07_cheng_li.pdf (7.5┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: