Correspondence map-aided neighbor embedding for image intra prediction

Abstract : This paper describes new image prediction methods based on neighbor embedding (NE) techniques. Neighbor embedding methods are used here to approximate an input block (the block to be predicted) in the image as a linear combination of K nearest neighbors. However, in order for the decoder to proceed similarly, the K nearest neighbors are found by computing distances between the known pixels in a causal neighborhood (called template) of the input block and the co-located pixels in candidate patches taken from a causal window. Similarly, the weights used for the linear approximation are computed in order to best approximate the template pixels. Although efficient, these methods suffer from limitations when the template and the block to be predicted are not correlated, e.g. in non homogenous texture areas. To cope with these limitations, this paper introduces new image prediction methods based on neighbor embedding techniques in which the K-NN search is done in two steps and aided, at the decoder, by a block correspondence map, hence the name Map-Aided Neighbor Embedding (MANE) method. Another optimized variant of this approach, called oMANE method, is also studied. In these methods, several alternatives have been also proposed for the K-NN search. The resulting prediction methods are shown to bring significant Rate-Distortion (RD) performance improvements when compared to H.264 Intra prediction modes (up to 44.75 % rate saving at low bit rates).
Type de document :
Article dans une revue
IEEE Transaction on Image Processing, 2012, Zhi-Quan (Tom) Luo, 2013, 22 (3), pp.1161-1174
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https://hal.inria.fr/hal-00755762
Contributeur : Safa Cherigui <>
Soumis le : mercredi 21 novembre 2012 - 19:45:39
Dernière modification le : vendredi 16 novembre 2018 - 01:40:24

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  • HAL Id : hal-00755762, version 1

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Safa Cherigui, Christine Guillemot, Dominique Thoreau, Philippe Guillotel, Patrick Pérez. Correspondence map-aided neighbor embedding for image intra prediction. IEEE Transaction on Image Processing, 2012, Zhi-Quan (Tom) Luo, 2013, 22 (3), pp.1161-1174. 〈hal-00755762〉

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