The Experts below are selected from a list of 189 Experts worldwide ranked by ideXlab platform
M Van Der Schaar - One of the best experts on this subject based on the ideXlab platform.
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weighted average spatio temporal Update Operator for subband video coding
International Conference on Image Processing, 2004Co-Authors: Christophe Tillier, Beatrice Pesquetpopescu, M Van Der SchaarAbstract:Spatio-temporal motion-compensated wavelet decomposition is an increasingly popular method for scalable video coding, with coding efficiency which is competitive with state-of-the-art nonscalable codecs. In this paper, we propose a new spatio-temporal Update Operator in the lifting scheme allowing efficient implementation of these temporal decompositions. We demonstrate its improved performance both theoretically, by exhibiting a decrease in the reconstruction error and by simulation results.
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ICIP - Weighted average spatio-temporal Update Operator for subband video coding
2004 International Conference on Image Processing 2004. ICIP '04., 1Co-Authors: Christophe Tillier, Beatrice Pesquet-popescu, M Van Der SchaarAbstract:Spatio-temporal motion-compensated wavelet decomposition is an increasingly popular method for scalable video coding, with coding efficiency which is competitive with state-of-the-art nonscalable codecs. In this paper, we propose a new spatio-temporal Update Operator in the lifting scheme allowing efficient implementation of these temporal decompositions. We demonstrate its improved performance both theoretically, by exhibiting a decrease in the reconstruction error and by simulation results.
Marc Antonini - One of the best experts on this subject based on the ideXlab platform.
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Sparsity-based optimization of two lifting-based wavelet transforms for semi-regular mesh compression
Computers and Graphics, 2012Co-Authors: Aymen Kammoun, Frédéric Payan, Marc AntoniniAbstract:This paper describes how to optimize two popular wavelet transforms for semi-regular meshes, using a lifting scheme. The objective is to adapt multiresolution analysis to the input mesh to improve its subsequent coding. Considering either the Butterfly- or the Loop-based lifting schemes, our algorithm finds at each resolution level an optimal prediction Operator P such that it minimizes the L1 norm of the wavelet coefficients. The Update Operator U is then recomputed in order to take into account the modifications to P. Experimental results show that our algorithm improves on state-of-the-art wavelet coders.
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Technical Section: Sparsity-based optimization of two lifting-based wavelet transforms for semi-regular mesh compression
Computers & Graphics, 2012Co-Authors: Aymen Kammoun, Frédéric Payan, Marc AntoniniAbstract:This paper describes how to optimize two popular wavelet transforms for semi-regular meshes, using a lifting scheme. The objective is to adapt multiresolution analysis to the input mesh to improve its subsequent coding. Considering either the Butterfly- or the Loop-based lifting schemes, our algorithm finds at each resolution level an optimal prediction Operator P such that it minimizes the L"1-norm of the wavelet coefficients. The Update Operator U is then recomputed in order to take into account the modifications to P. Experimental results show that our algorithm improves on state-of-the-art wavelet coders.
Christophe Tillier - One of the best experts on this subject based on the ideXlab platform.
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weighted average spatio temporal Update Operator for subband video coding
International Conference on Image Processing, 2004Co-Authors: Christophe Tillier, Beatrice Pesquetpopescu, M Van Der SchaarAbstract:Spatio-temporal motion-compensated wavelet decomposition is an increasingly popular method for scalable video coding, with coding efficiency which is competitive with state-of-the-art nonscalable codecs. In this paper, we propose a new spatio-temporal Update Operator in the lifting scheme allowing efficient implementation of these temporal decompositions. We demonstrate its improved performance both theoretically, by exhibiting a decrease in the reconstruction error and by simulation results.
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ICIP - Weighted average spatio-temporal Update Operator for subband video coding
2004 International Conference on Image Processing 2004. ICIP '04., 1Co-Authors: Christophe Tillier, Beatrice Pesquet-popescu, M Van Der SchaarAbstract:Spatio-temporal motion-compensated wavelet decomposition is an increasingly popular method for scalable video coding, with coding efficiency which is competitive with state-of-the-art nonscalable codecs. In this paper, we propose a new spatio-temporal Update Operator in the lifting scheme allowing efficient implementation of these temporal decompositions. We demonstrate its improved performance both theoretically, by exhibiting a decrease in the reconstruction error and by simulation results.
L. De Raedt - One of the best experts on this subject based on the ideXlab platform.
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Bellman goes Relational (extended abstract)
2004Co-Authors: M. Van Otterlo, Kristian Kersting, L. De RaedtAbstract:We introduce REBEL, a relational Bellman Update Operator that can be used for Markov Decision Processes in - possibly infinite - relational domains. Using REBEL we develop a relational value iteration algorithm.
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ICML - Bellman goes relational
Twenty-first international conference on Machine learning - ICML '04, 2004Co-Authors: Kristian Kersting, M. Van Otterlo, L. De RaedtAbstract:Motivated by the interest in relational reinforcement learning, we introduce a novel relational Bellman Update Operator called REBEL. It employs a constraint logic programming language to compactly represent Markov decision processes over relational domains. Using REBEL, a novel value iteration algorithm is developed in which abstraction (over states and actions) plays a major role. This framework provides new insights into relational reinforcement learning. Convergence results as well as experiments are presented.
Aymen Kammoun - One of the best experts on this subject based on the ideXlab platform.
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Sparsity-based optimization of two lifting-based wavelet transforms for semi-regular mesh compression
Computers and Graphics, 2012Co-Authors: Aymen Kammoun, Frédéric Payan, Marc AntoniniAbstract:This paper describes how to optimize two popular wavelet transforms for semi-regular meshes, using a lifting scheme. The objective is to adapt multiresolution analysis to the input mesh to improve its subsequent coding. Considering either the Butterfly- or the Loop-based lifting schemes, our algorithm finds at each resolution level an optimal prediction Operator P such that it minimizes the L1 norm of the wavelet coefficients. The Update Operator U is then recomputed in order to take into account the modifications to P. Experimental results show that our algorithm improves on state-of-the-art wavelet coders.
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Technical Section: Sparsity-based optimization of two lifting-based wavelet transforms for semi-regular mesh compression
Computers & Graphics, 2012Co-Authors: Aymen Kammoun, Frédéric Payan, Marc AntoniniAbstract:This paper describes how to optimize two popular wavelet transforms for semi-regular meshes, using a lifting scheme. The objective is to adapt multiresolution analysis to the input mesh to improve its subsequent coding. Considering either the Butterfly- or the Loop-based lifting schemes, our algorithm finds at each resolution level an optimal prediction Operator P such that it minimizes the L"1-norm of the wavelet coefficients. The Update Operator U is then recomputed in order to take into account the modifications to P. Experimental results show that our algorithm improves on state-of-the-art wavelet coders.
