The Experts below are selected from a list of 57621 Experts worldwide ranked by ideXlab platform

Sanjit K. Mitra - One of the best experts on this subject based on the ideXlab platform.

  • A source and channel-Coding Framework for vector-based data hiding in video
    IEEE Transactions on Circuits and Systems for Video Technology, 2000
    Co-Authors: Debargha Mukherjee, Jong Jin Chae, Sanjit K. Mitra
    Abstract:

    Digital data hiding is a technology being developed for multimedia services, where significant amounts of secure data is invisibly hidden inside a host data source by the owner, for retrieval only by those authorized. The hidden data should be recoverable even after the host has undergone standard transformations, such as compression. In this paper, we present a source and channel Coding Framework for data hiding, allowing any tradeoff between the visibility of distortions introduced, the amount of data embedded, and the degree of robustness to noise. The secure data is source coded by vector quantization, and the indices obtained in the process are embedded in the host video using orthogonal transform domain vector perturbations. Transform coefficients of the host are grouped into vectors and perturbed using noise-resilient channel codes derived from multidimensional lattices. The perturbations are constrained by a maximum allowable mean-squared error that can be introduced in the host. Channel-optimized VQ can be used for increased robustness to noise. The generic approach is readily adapted to make retrieval possible for applications where the original host is not available to the retriever. The secure data in our implementations are low spatial and temporal resolution video, and sampled speech, while the host data is QCIF video. The host video with the embedded data is H.263 compressed, before attempting retrieval of the hidden video and speech from the reconstructed video. The quality of the extracted video and speech is shown for varying compression ratios of the host video.

Jianfeng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • approximate code a cost effective erasure Coding Framework for tiered video storage in cloud systems
    International Conference on Parallel Processing, 2019
    Co-Authors: Huayi Jin, Xin Xie, Minyi Guo, Hao Lin, Jianfeng Zhang
    Abstract:

    Nowadays massive video data are stored in cloud storage systems, which are generated by various applications such as autonomous driving, news media, security monitoring, etc. Meanwhile, erasure Coding is a popular technique in cloud storage to provide both high reliability and low monetary cost, where triple disk failure tolerant arrays (3DFTs) is a typical choice. Therefore, how to minimize the storage cost of video data in 3DFTs is a challenge for cloud storage systems. Although there are several solutions like approximate storage technique, they cannot guarantee low storage cost and high data reliability concurrently. To address this challenge, in this paper, we propose Approximate Code, which is an erasure Coding Framework for tiered video storage in cloud systems. The key idea of Approximate Code is distinguishing the important and unimportant data with different capabilities of fault tolerance. On one hand, for important data, Approximate Code provides triple parities to ensure high reliability. On the other hand, single/double parities are applied for unimportant data, which can save the storage cost and accelerate the recovery process. To demonstrate the effectiveness of Approximate Code, we conduct several experiments in Hadoop systems. The results show that, compared to traditional 3DFTs using various erasure codes such as RS, LRC, STAR and TIP-Code, Approximate Code reduces the number of parities by up to 55%, saves the storage cost by up to 20.8% and increase the recovery speed by up to 4.7X when double nodes fail.

  • ICPP - Approximate Code: A Cost-Effective Erasure Coding Framework for Tiered Video Storage in Cloud Systems
    Proceedings of the 48th International Conference on Parallel Processing, 2019
    Co-Authors: Huayi Jin, Xin Xie, Minyi Guo, Lin Hao, Jianfeng Zhang
    Abstract:

    Nowadays massive video data are stored in cloud storage systems, which are generated by various applications such as autonomous driving, news media, security monitoring, etc. Meanwhile, erasure Coding is a popular technique in cloud storage to provide both high reliability and low monetary cost, where triple disk failure tolerant arrays (3DFTs) is a typical choice. Therefore, how to minimize the storage cost of video data in 3DFTs is a challenge for cloud storage systems. Although there are several solutions like approximate storage technique, they cannot guarantee low storage cost and high data reliability concurrently. To address this challenge, in this paper, we propose Approximate Code, which is an erasure Coding Framework for tiered video storage in cloud systems. The key idea of Approximate Code is distinguishing the important and unimportant data with different capabilities of fault tolerance. On one hand, for important data, Approximate Code provides triple parities to ensure high reliability. On the other hand, single/double parities are applied for unimportant data, which can save the storage cost and accelerate the recovery process. To demonstrate the effectiveness of Approximate Code, we conduct several experiments in Hadoop systems. The results show that, compared to traditional 3DFTs using various erasure codes such as RS, LRC, STAR and TIP-Code, Approximate Code reduces the number of parities by up to 55%, saves the storage cost by up to 20.8% and increase the recovery speed by up to 4.7X when double nodes fail.

Thomas Plotz - One of the best experts on this subject based on the ideXlab platform.

  • using unlabeled data in a sparse Coding Framework for human activity recognition
    Pervasive and Mobile Computing, 2014
    Co-Authors: Sourav Bhattacharya, Petteri Nurmi, Nils Y Hammerla, Thomas Plotz
    Abstract:

    We propose a sparse-Coding Framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is easy to obtain, e.g., through contemporary smartphones carried by users as they go about their everyday activities.Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data.We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on a transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-Coding Framework demonstrates better performance than the state-of-the-art in supervised learning approaches. More importantly, we show the practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities of daily living.

  • towards using unlabeled data in a sparse Coding Framework for human activity recognition
    arXiv: Learning, 2013
    Co-Authors: Sourav Bhattacharya, Petteri Nurmi, Nils Y Hammerla, Thomas Plotz
    Abstract:

    We propose a sparse-Coding Framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes extremely well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is trivial to obtain, e.g., through contemporary smartphones carried by users as they go about their everyday activities. Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data. We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-Coding Framework significantly outperforms the state-of-the-art in supervised learning approaches. Furthermore, we demonstrate the great practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities in daily living.

Yao Wang - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Video Coding Framework Using a Self-Adaptive Dictionary
    IEEE Transactions on Circuits and Systems for Video Technology, 2018
    Co-Authors: Yuanyi Xue, Yao Wang
    Abstract:

    In this paper, we propose to use a self-adaptive redundant dictionary, consisting of all possible inter and intra prediction candidates, to directly represent the frame blocks in a video sequence. The self-adaptive dictionary generalizes the conventional predictive Coding approach by allowing adaptive linear combinations of prediction candidates, which is solved by an rate-distortion aware L0-norm minimization problem using orthogonal least squares (OLS). To overcome the inefficiency in quantizing and Coding coefficients corresponding to correlated chosen atoms, we orthonormalize the chosen atoms recursively as part of OLS process. We further propose a two-stage video Coding Framework, in which a second stage codes the residual from the chosen atoms using a modified discrete cosine transform (DCT) dictionary that is adaptively orthonormalized with respect to the subspace spanned by the first stage atoms. To determine the transition from the first stage to the second stage, we propose a rate-distortion (RD) aware adaptive switching algorithm. The proposed Framework is further extended to accommodate variable block sizes ( $16\times 16$ , $8\times 8$ , and $4\times 4$ ), and the partition mode is derived by a fast partition mode decision algorithm. A context-adaptive binary arithmetic entropy coder is designed to code the symbols of the proposed Coding Framework. The proposed coder shows competitive, and in some cases better RD performance, compared with the HEVC video Coding standard for P-frames.

  • a two stage video Coding Framework with both self adaptive redundant dictionary and adaptively orthonormalized dct basis
    arXiv: Multimedia, 2015
    Co-Authors: Yi Zhou, Yao Wang
    Abstract:

    In this work, we propose a two-stage video Coding Framework, as an extension of our previous one-stage Framework in [1]. The two-stage Frameworks consists two different dictionaries. Specifically, the first stage directly finds the sparse representation of a block with a self-adaptive dictionary consisting of all possible inter-prediction candidates by solving an L0-norm minimization problem using an improved orthogonal matching pursuit with embedded orthonormalization (eOMP) algorithm, and the second stage codes the residual using DCT dictionary adaptively orthonormalized to the subspace spanned by the first stage atoms. The transition of the first stage and the second stage is determined based on both stages' quantization stepsizes and a threshold. We further propose a complete context adaptive entropy coder to efficiently code the locations and the coefficients of chosen first stage atoms. Simulation results show that the proposed coder significantly improves the RD performance over our previous one-stage coder. More importantly, the two-stage coder, using a fixed block size and inter-prediction only, outperforms the H.264 coder (x264) and is competitive with the HEVC reference coder (HM) over a large rate range.

  • A two-stage video Coding Framework with both self-adaptive redundant dictionary and adaptively orthonormalized DCT basis
    2015 IEEE International Conference on Image Processing (ICIP), 2015
    Co-Authors: Yi Zhou, Yao Wang
    Abstract:

    In this work, we propose a two-stage video Coding Framework, as an extension of our previous one-stage Framework in [1]. The two-stage Frameworks consists two different dictionaries. Specifically, the first stage directly finds the sparse representation of a block with a self-adaptive dictionary consisting of all possible inter-prediction candidates by solving an L0-norm minimization problem using orthogonal least squares (OLS), and the second stage codes the residual using altered DCT dictionary orthonormalized to the subspace spanned by the first stage atoms. The transition of the first stage and the second stage is adaptively determined based on the estimated residual reduction per bit. We further propose a complete context adaptive entropy coder to efficiently code the locations and the coefficients of chosen first stage atoms. Simulation results show that the proposed coder significantly improves the RD performance over our previous one-stage coder. More importantly, the two-stage coder, using a fixed block size and inter-prediction only, outperforms the H.264 coder (x264) and is competitive with the HEVC reference coder (HM) over a large rate range.

  • ICIP - A two-stage video Coding Framework with both self-adaptive redundant dictionary and adaptively orthonormalized DCT basis
    2015 IEEE International Conference on Image Processing (ICIP), 2015
    Co-Authors: Yuanyi Xue, Yi Zhou, Yao Wang
    Abstract:

    In this work, we propose a two-stage video Coding Framework, as an extension of our previous one-stage Framework in [1]. The two-stage Frameworks consists two different dictionaries. Specifically, the first stage directly finds the sparse representation of a block with a self-adaptive dictionary consisting of all possible inter-prediction candidates by solving an L0-norm minimization problem using orthogonal least squares (OLS), and the second stage codes the residual using altered DCT dictionary orthonormalized to the subspace spanned by the first stage atoms. The transition of the first stage and the second stage is adaptively determined based on the estimated residual reduction per bit. We further propose a complete context adaptive entropy coder to efficiently code the locations and the coefficients of chosen first stage atoms. Simulation results show that the proposed coder significantly improves the RD performance over our previous one-stage coder. More importantly, the two-stage coder, using a fixed block size and inter-prediction only, outperforms the H.264 coder (x264) and is competitive with the HEVC reference coder (HM) over a large rate range.

Bilin Sun - One of the best experts on this subject based on the ideXlab platform.

  • a weighted sparse Coding Framework for saliency detection
    Computer Vision and Pattern Recognition, 2015
    Co-Authors: Bilin Sun
    Abstract:

    There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution Frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection Framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential foreground superpixels to build a primitive saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.

  • CVPR - A weighted sparse Coding Framework for saliency detection
    2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
    Co-Authors: Bilin Sun
    Abstract:

    There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution Frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection Framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential foreground superpixels to build a primitive saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.