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Proceedings Paper

Predictive Compression and Denoising with Overcomplete Decompositions: A Simple Way to Reject Structured Interference
Author(s): Oztan Harmanci; Gang Hua; Onur G. Guleryuz
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Paper Abstract

In this paper we propose a prediction method that is geared toward forming successful estimates of a signal based on a correlated anchor signal contaminated with complex interference. The interference model is based on real-life, and it involves intensity modulations, linear distortions, structured clutter, and white noise just to name a few. The proposed method first transforms signals to an over-complete domain where we assume sparse decompositions. In this sparse domain, we show that very simple predictors can be designed to perform efficient prediction. The parameters of these predictors are derived from causal information, enabling completely automated and blind operation. The utilized over-complete representation allows multiple predictions for each sample in signal domain, which are averaged and combined into a single prediction. Experimental results on images and video frames show that the proposed method can provide successful predictions under a variety of complex transitions, such as cross-fades, brightness changes, focus variations, and other complex distortions. The proposed prediction method is also implemented to operate inside a state-of-the-art video compression codec and results show significant improvements on scenes that are hard to encode using traditional prediction techniques.

Paper Details

Date Published: 27 September 2007
PDF: 15 pages
Proc. SPIE 6701, Wavelets XII, 67011E (27 September 2007); doi: 10.1117/12.735220
Show Author Affiliations
Oztan Harmanci, DoCoMo USA Labs., Inc. (United States)
Gang Hua, Rice Univ. (United States)
Onur G. Guleryuz, DoCoMo USA Labs., Inc. (United States)

Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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