The Experts below are selected from a list of 165324 Experts worldwide ranked by ideXlab platform
Hongkai Xiong - One of the best experts on this subject based on the ideXlab platform.
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Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding
IEEE Transactions on Image Processing, 2014Co-Authors: Wenrui Dai, Hongkai Xiong, Jia Wang, Yuan F. ZhengAbstract:Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a Set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
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Structured Set intra prediction with discriminative learning in a max-margin markov network for high efficiency video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2013Co-Authors: Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, Chang Wen ChenAbstract:This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The Structured Set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a Set of predictions. Specifically, the proposed model concurrently optimizes a Set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
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optimal intra coding of hevc by Structured Set prediction mode with discriminative learning
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the Set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called Structured Set prediction model incorporates max-margin Markov network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov network is adaptively derived to maintain the coherence of Set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the Set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
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discriminative Structured Set prediction modeling with max margin markov network for optimal lossless image coding
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:In this paper, we investigate and propose a novel prediction model for lossless image coding in which the optimal correlated prediction for block of pixels are simultaneously obtained in the sense of the least code length. It not only utilizes the spatial statistical correlation for the optimal prediction directly based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Besides the discriminative adaptive pixel-wise prediction, the Markov network is adaptively derived to maintain the coherence of prediction in the blocks and seek the concurrent optimization of Set of prediction by relating the loss function to actual code length. The prediction error is shown to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we apply the proposed model into lossless image coding and experimental results show that the proposed scheme outperforms the best prediction scheme reported.
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VCIP - Optimal intra coding of HEVC by Structured Set prediction mode with discriminative learning
2012 Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the Set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called Structured Set prediction model incorporates max-margin Markov network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov network is adaptively derived to maintain the coherence of Set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the Set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
Wenrui Dai - One of the best experts on this subject based on the ideXlab platform.
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Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding
IEEE Transactions on Image Processing, 2014Co-Authors: Wenrui Dai, Hongkai Xiong, Jia Wang, Yuan F. ZhengAbstract:Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a Set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
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Structured Set intra prediction with discriminative learning in a max-margin markov network for high efficiency video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2013Co-Authors: Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, Chang Wen ChenAbstract:This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The Structured Set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a Set of predictions. Specifically, the proposed model concurrently optimizes a Set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
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optimal intra coding of hevc by Structured Set prediction mode with discriminative learning
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the Set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called Structured Set prediction model incorporates max-margin Markov network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov network is adaptively derived to maintain the coherence of Set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the Set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
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discriminative Structured Set prediction modeling with max margin markov network for optimal lossless image coding
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:In this paper, we investigate and propose a novel prediction model for lossless image coding in which the optimal correlated prediction for block of pixels are simultaneously obtained in the sense of the least code length. It not only utilizes the spatial statistical correlation for the optimal prediction directly based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Besides the discriminative adaptive pixel-wise prediction, the Markov network is adaptively derived to maintain the coherence of prediction in the blocks and seek the concurrent optimization of Set of prediction by relating the loss function to actual code length. The prediction error is shown to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we apply the proposed model into lossless image coding and experimental results show that the proposed scheme outperforms the best prediction scheme reported.
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VCIP - Optimal intra coding of HEVC by Structured Set prediction mode with discriminative learning
2012 Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the Set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called Structured Set prediction model incorporates max-margin Markov network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov network is adaptively derived to maintain the coherence of Set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the Set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
Xiangkun Shen - One of the best experts on this subject based on the ideXlab platform.
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stochastic makespan minimization in Structured Set systems extended abstract
Integer Programming and Combinatorial Optimization, 2020Co-Authors: Anupam Gupta, Amit Kumar, Viswanath Nagarajan, Xiangkun ShenAbstract:We study stochastic combinatorial optimization problems where the objective is to minimize the expected maximum load (a.k.a. the makespan). In this framework, we have a Set of n tasks and m resources, where each task j uses some subSet of the resources. Tasks have random sizes \(X_j\), and our goal is to non-adaptively select t tasks to minimize the expected maximum load over all resources, where the load on any resource i is the total size of all selected tasks that use i. For example, given a Set of intervals in time, with each interval j having random load \(X_j\), how do we choose t intervals to minimize the expected maximum load at any time? Our technique is also applicable to other problems with some geometric structure in the relation between tasks and resources; e.g., packing paths, rectangles, and “fat” objects. Specifically, we give an \(O(\log \log m)\)-approximation algorithm for all these problems.
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IPCO - Stochastic Makespan Minimization in Structured Set Systems (Extended Abstract)
Integer Programming and Combinatorial Optimization, 2020Co-Authors: Anupam Gupta, Amit Kumar, Viswanath Nagarajan, Xiangkun ShenAbstract:We study stochastic combinatorial optimization problems where the objective is to minimize the expected maximum load (a.k.a. the makespan). In this framework, we have a Set of n tasks and m resources, where each task j uses some subSet of the resources. Tasks have random sizes \(X_j\), and our goal is to non-adaptively select t tasks to minimize the expected maximum load over all resources, where the load on any resource i is the total size of all selected tasks that use i. For example, given a Set of intervals in time, with each interval j having random load \(X_j\), how do we choose t intervals to minimize the expected maximum load at any time? Our technique is also applicable to other problems with some geometric structure in the relation between tasks and resources; e.g., packing paths, rectangles, and “fat” objects. Specifically, we give an \(O(\log \log m)\)-approximation algorithm for all these problems.
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Stochastic Makespan Minimization in Structured Set Systems.
arXiv: Data Structures and Algorithms, 2020Co-Authors: Anupam Gupta, Amit Kumar, Viswanath Nagarajan, Xiangkun ShenAbstract:We study stochastic combinatorial optimization problems where the objective is to minimize the expected maximum load (a.k.a. the makespan). In this framework, we have a Set of n tasks and m resources, where each task j uses some subSet of the resources. Tasks have random sizes X_j, and our goal is to non-adaptively select t tasks to minimize the expected maximum load over all resources, where the load on any resource i is the total size of all selected tasks that use i. For example, given a Set of intervals in time, with each interval j having random load X_j, how do we choose t intervals to minimize the expected maximum load at any time? Our technique is also applicable to other problems with some geometric structure in the relation between tasks and resources; e.g., packing paths, rectangles, and "fat" objects. Specifically, we give an O(\log\log m)-approximation algorithm for all these problems. Our approach uses a strong LP relaxation using the cumulant generating functions of the random variables. We also show that this LP has an \Omega(\log^* m) integrality gap even for the problem of selecting intervals on a line. Moreover, we show logarithmic gaps for problems without geometric structure, showing that some structure is needed to get good results using these techniques.
Matteo Valoriani - One of the best experts on this subject based on the ideXlab platform.
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IDC - Designing and evaluating touchless playful interaction for ASD children
Proceedings of the 2014 conference on Interaction design and children - IDC '14, 2014Co-Authors: Laura Bartoli, Franca Garzotto, Mirko Gelsomini, Luigi Oliveto, Matteo ValorianiAbstract:Limited studies exist that explore motionbased touchless applications for children with ASD (Autism Spectrum Disorder) and investigate their design issues and the benefits they can bring to this target group. The paper reports a Structured Set of design guidelines that distill our experience gained from empirical studies and collaborations with therapeutic centers. These heuristics informed the design of three touchless games that were evaluated in a controlled study involving medium functioning ASD children at a therapeutic center. Our findings confirm the potential of motionbased touchless applications games for technologyenhanced interventions for this target group.
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designing and evaluating touchless playful interaction for asd children
Interaction Design and Children, 2014Co-Authors: Laura Artoli, Franca Garzotto, Mirko Gelsomini, Luigi Oliveto, Matteo ValorianiAbstract:Limited studies exist that explore motionbased touchless applications for children with ASD (Autism Spectrum Disorder) and investigate their design issues and the benefits they can bring to this target group. The paper reports a Structured Set of design guidelines that distill our experience gained from empirical studies and collaborations with therapeutic centers. These heuristics informed the design of three touchless games that were evaluated in a controlled study involving medium functioning ASD children at a therapeutic center. Our findings confirm the potential of motionbased touchless applications games for technologyenhanced interventions for this target group.
Chang Wen Chen - One of the best experts on this subject based on the ideXlab platform.
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Structured Set intra prediction with discriminative learning in a max-margin markov network for high efficiency video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2013Co-Authors: Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, Chang Wen ChenAbstract:This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The Structured Set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a Set of predictions. Specifically, the proposed model concurrently optimizes a Set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
