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

Christine Guillemot 1 Safa Chérigui 1, 2 Dominique Thoreau 2
1 Sirocco - Analysis representation, compression and communication of visual data
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique
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.
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https://hal.inria.fr/hal-00876123
Contributor : Christine Guillemot <>
Submitted on : Wednesday, October 23, 2013 - 5:19:17 PM
Last modification on : Friday, July 10, 2020 - 4:26:39 PM

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  • HAL Id : hal-00876123, version 1

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Christine Guillemot, Safa Chérigui, Dominique Thoreau. K-NN search using local learning based on regression for image prediction with neighbor embedding. IEEE Intl. Conf. on Acoustics and Signal Processing (IEEE-ICASSP), May 2013, Vancouver, United States. ⟨hal-00876123⟩

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