By Topic

Spatial Sparsity Induced Temporal Prediction for Hybrid Video Compression

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Gang Hua ; Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX ; Guleryuz, O.G.

In this paper we propose a new motion compensated prediction technique that enables successful predictive encoding during fades, blended scenes, temporally decorrelated noise, and many other temporal evolutions which force predictors used in traditional hybrid video coders to fail. We model reference frame blocks to be used in motion compensated prediction as consisting of two superimposed parts: one part that is relevant for prediction and another part that is not relevant. By performing prediction in a domain where the video frames are spatially sparse, our work allows the automatic isolation of the prediction-relevant parts. These are then used to enable better prediction than would be possible otherwise. Our sparsity induced prediction algorithm (SIP) generates successful predictors by exploiting the non-convex structure of the sets that natural images and video frames lie in. Correctly determining this non-convexity through sparse representations allows better performance in hybrid video codecs equipped with the proposed work

Published in:

Data Compression Conference, 2007. DCC '07

Date of Conference:

27-29 March 2007