MAP-AIDED LOCALLY LINEAR EMBEDDING METHODS FOR IMAGE PREDICTION

Abstract : Image prediction methods based on data dimensionality reduction techniques have been introduced in [1]. Although efficient, these methods suffer from limitations when the block to be predicted and its neighborhood (or template) are not correlated, e.g. in non homogenous texture areas. To cope with these limitations, this paper introduces new image prediction methods based on locally linear embedding (LLE) technique in which the required K-NN search is aided, at the decoder, by a block correspondence map, hence the name Map-Aided Locally Linear Embedding (MALLE) method. Another optimized variant of this approach, called oMALLE method, is also studied. The resulting prediction methods are shown to bring significant Rate-Distortion (RD) performance improvements when compared to H.264 Intra prediction modes (up to 40.78 % rate saving at low bit rates).
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https://hal.inria.fr/hal-00749966
Contributor : Safa Cherigui <>
Submitted on : Thursday, November 8, 2012 - 4:46:05 PM
Last modification on : Friday, November 16, 2018 - 1:39:36 AM

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  • HAL Id : hal-00749966, version 1

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Safa Cherigui, Christine Guillemot, Dominique Thoreau, Philippe Guillotel, Patrick Pérez. MAP-AIDED LOCALLY LINEAR EMBEDDING METHODS FOR IMAGE PREDICTION. ICIP'12 - International Conference on Image Processing, Sep 2012, Orlando, United States. ⟨hal-00749966⟩

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