NDLI: K-NN search using local learning based on regression for neighbor embedding-based image prediction

Content Provider	IEEE Xplore Digital Library
Author	Guillemot, C. Cherigui, S. Thoreau, D.
Copyright Year	2013
Description	Author affiliation: INRIA, Campus Univ. de Beaulieu, Rennes, France (Guillemot, C.) \|\| Technicolor R & D France, Cesson-Sévigné, France (Cherigui, S.; Thoreau, D.)
Abstract	The paper describes a K-NN search method aided by local learning of subspace mappings for the problem of neighbor-embedding based image Intra prediction. The local learning of subspace mappings relies on multivariate linear regression. The method is used jointly with Locally Linear Embedding (LLE) as well as with a method inspired from Non Local Means (NLM) for prediction. Linear and kernel ridge regression are also considered directly for predicting the unknown pixels. Rate-distortion performances are then given in comparison with Intra prediction using LLE and classical K-NN search, as well as in comparison with H.264 Intra prediction modes.
Starting Page	2006
Ending Page	2010
File Size	323183
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638005
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximations Prediction algorithms Kernel Linear regression Search problems Training data dimensionality reduction Image compression prediction linear regression
Content Type	Text
Resource Type	Article

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