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Locally Linear Embedding Methods for Inter Image Prediction

Martin Alain 1, 2 Safa Chérigui 1, 2 Christine Guillemot 1 Dominique Thoreau 2 Philippe Guillotel 2 
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Image prediction methods based on data dimensionality reduction techniques have been recently introduced for still images. These techniques have been proven efficient, especially when used in a H.264 framework. This paper introduces a natural extension of this work from spatial prediction to temporal prediction. Locally Linear Embedding and Optimized Map-Aided Locally Linear Embedding methods are adapted within H.264 in order to improve inter image prediction. The resulting prediction methods are compared with the H.264 motion estimation/compensation method, template matching methods and Adaptive Interpolation Filters, and are shown to bring significant Rate-Distortion performance improvements (up to 20.21 % rate saving with respect to H.264).
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https://hal.inria.fr/hal-00875941
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Submitted on : Thursday, October 24, 2013 - 3:06:14 PM
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  • HAL Id : hal-00875941, version 1

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Martin Alain, Safa Chérigui, Christine Guillemot, Dominique Thoreau, Philippe Guillotel. Locally Linear Embedding Methods for Inter Image Prediction. ICIP - IEEE International Conference on Image Processing - 2013, Sep 2013, Melbourne, Australia. ⟨hal-00875941⟩

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