In this paper, we propose novel design schemes for the operation of Decoded Picture Buffer (DPB) including reference picture re-ordering, marking process, and reference picture list construction to perform an efficien...
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ISBN:
(纸本)9781479975440
In this paper, we propose novel design schemes for the operation of Decoded Picture Buffer (DPB) including reference picture re-ordering, marking process, and reference picture list construction to perform an efficient scalable multi-view video coding. Extensive simulations show that the proposed method can provide advanced compression efficiency and improved video quality measured in terms of BD-Rate and BD-PSNR for the scalable multi-view video coding.
In recent years, technology-assisted review (TAR) has become an increasingly important component of the document review process in litigation discovery. This is fueled largely by dramatic growth in data volumes that m...
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In recent years, technology-assisted review (TAR) has become an increasingly important component of the document review process in litigation discovery. This is fueled largely by dramatic growth in data volumes that may be associated with many matters and investigations. Potential review populations frequently exceed several hundred thousands documents, and document counts in the millions are not uncommon. Budgetary and/or time constraints often make a once traditional linear review of these populations impractical, if not impossible - which made "predictive coding" the most discussed TAR approach in recent years. A key challenge in any predictive coding approach is striking the appropriate balance in training the system. The goal is to minimize the time that Subject Matter Experts spend in training the system, while making sure that they perform enough training to achieve acceptable classification performance over the entire review population. Recent research demonstrates that Support Vector Machines (SVM) perform very well in finding a compact, yet effective, training dataset in an iterative fashion using batch-mode active learning. However, this research is limited. Additionally, these efforts have not led to a principled approach for determining the stabilization of the active learning process. In this paper, we propose and compare several batch-mode active learning methods which are integrated within SVM learning algorithm. We also propose methods for determining the stabilization of the active learning method. Experimental results on a set of large-scale, real-life legal document collections validate the superiority of our method over the existing methods for this task.
Distributed visual analysis applications, such as mobile visual search or Visual Sensor Networks (VSNs) require the transmission of visual content on a bandwidth-limited network, from a peripheral node to a processing...
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ISBN:
(纸本)9781479983407
Distributed visual analysis applications, such as mobile visual search or Visual Sensor Networks (VSNs) require the transmission of visual content on a bandwidth-limited network, from a peripheral node to a processing unit. Traditionally, a "Compress-Then-Analyze" approach has been pursued, in which sensing nodes acquire and encode the pixel-level representation of the visual content, that is subsequently transmitted to a sink node in order to be processed. This approach might not represent the most effective solution, since several analysis applications leverage a compact representation of the content, thus resulting in an inefficient usage of network resources. Furthermore, coding artifacts might significantly impact the accuracy of the visual task at hand. To tackle such limitations, an orthogonal approach named "Analyze-Then-Compress" has been proposed [1]. According to such a paradigm, sensing nodes are responsible for the extraction of visual features, that are encoded and transmitted to a sink node for further processing. In spite of improved task efficiency, such paradigm implies the central processing node not being able to reconstruct a pixel-level representation of the visual content. In this paper we propose an effective compromise between the two paradigms, namely "Hybrid-Analyze-Then-Compress" (HATC) that aims at jointly encoding visual content and local image features. Furthermore, we show how a target tradeoff between image quality and task accuracy might be achieved by accurately allocating the bitrate to either visual content or local features.
We study sequential transmission of Gauss-Markov sources over erasure channels under a zero decoding delay constraint. A two-stage coding scheme which can be described as a hybrid between predictive coding with limite...
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ISBN:
(纸本)9781467377058
We study sequential transmission of Gauss-Markov sources over erasure channels under a zero decoding delay constraint. A two-stage coding scheme which can be described as a hybrid between predictive coding with limited past and quantization & binning is proposed. This scheme can achieve significant performance gains over baseline schemes in simulations involving i.i.d. erasure channels, and in certain regimes can attain performance close to a fundamental lower bound. We consider an information theoretic model for streaming that explains the weakness of baseline schemes (e.g., predictive coding, memoryless binning, etc.) and illustrates the advantage of our proposed hybrid scheme over these. Techniques from multi-terminal source coding are used to derive a new lower bound on the compression rate and identify cases when the hybrid coding scheme is close to optimal. We discuss qualitatively the interplay between the parameters of our information theoretic model and the statistical models used in simulations.
The use of forward models (mechanisms that predict the future state of a system) is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accoun...
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The use of forward models (mechanisms that predict the future state of a system) is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model (AFM) account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model (IFM) account, forward models lie at the heart of all forms of perception and action. We compare these neighbouring but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches.
It was recently shown that delta-sigma quantization (DSQ) can be used for optimal multiple description (MD) coding of Gaussian sources. The DSQ scheme combined oversampling, prediction, and noise-shaping in order to t...
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It was recently shown that delta-sigma quantization (DSQ) can be used for optimal multiple description (MD) coding of Gaussian sources. The DSQ scheme combined oversampling, prediction, and noise-shaping in order to trade off side distortion for central distortion in MD coding. It was shown that asymptotically in the dimensions of the resampling, prediction, and noise-shaping filters as well as asymptotically in the quantizer dimensions, all rate-distortion points on the symmetric quadratic Gaussian MD rate-distortion function could be achieved. In this work, we show that this somewhat theoretical framework is suitable for practical low-delay MD audio coding. In particular, we design a practical MD audio coder with two descriptions and provide simulations on real audio data. The simulations demonstrate that even when using low-dimensional noise-shaping, prediction, and resampling filters, it is possible to obtain good quality audio in the presence of packet losses. Simulations on real audio reveal that, contrary to existing designs, it is straightforward to obtain a large number of trade-off points between side distortion and central distortion, which makes the proposed coder suitable for a wide range of applications.
This paper proposes a lossless to lossy compression scheme for hyperspectral images based on dual-tree Binary Embedded Zerotree Wavelet (BEZW) algorithm. The algorithm adapts Karhunen-Loeve Transform and Discrete Wave...
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This paper proposes a lossless to lossy compression scheme for hyperspectral images based on dual-tree Binary Embedded Zerotree Wavelet (BEZW) algorithm. The algorithm adapts Karhunen-Loeve Transform and Discrete Wavelet Transform to achieve 3-D integer reversible hybrid transform and decorrelate spectral and spatial data. Since statistics of the hyperspectral image are not symmetrical, the asymmetrical dual-tree structure is introduced. The 3-D BEZW algorithm compresses hyperspectral images by implementing progressive bitplane coding. The lossless and lossy compression performance is compared with other state-of-the-art predictive coding and transform-based coding algorithms on Airborne Visible/Infrared Imaging Spectrometer images. Results show that the 3-D-BEZW lossless compression performance is comparable with the best predictive algorithms, while its computational cost is comparable with those of transform-based algorithms.
The mental states of other people are components of the external world that modulate the activity of our sensory epithelia. Recent probabilistic frameworks that cast perception as unconscious inference on the external...
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The mental states of other people are components of the external world that modulate the activity of our sensory epithelia. Recent probabilistic frameworks that cast perception as unconscious inference on the external causes of sensory input can thus be expanded to enfold the brain's representation of others' mental states. This paper examines this subject in the context of the debate concerning the extent to which we have perceptual awareness of other minds. In particular, we suggest that the notion of perceptual presence helps to refine this debate: are others' mental states experienced as veridical qualities of the perceptual world around us? This experiential aspect of social cognition may be central to conditions such as autism spectrum disorder, where representations of others' mental states seem to be selectively compromised. Importantly, recent work ties perceptual presence to the counterfactual predictions of hierarchical generative models that are suggested to perform unconscious inference in the brain. This enables a characterisation of mental state representations in terms of their associated counterfactual predictions, allowing a distinction between spontaneous and explicit forms of mentalising within the framework of predictive processing. This leads to a hypothesis that social cognition in autism spectrum disorder is characterised by a diminished set of counterfactual predictions and the reduced perceptual presence of others' mental states. (C) 2015 Elsevier Inc. All rights reserved.
Aron Gurwitsch's theory of the structure and dynamics of consciousness has much to offer contemporary theorizing about consciousness and its basis in the embodied brain. On Gurwitsch's account, as we develop i...
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Aron Gurwitsch's theory of the structure and dynamics of consciousness has much to offer contemporary theorizing about consciousness and its basis in the embodied brain. On Gurwitsch's account, as we develop it, the field of consciousness has a variable sized focus or "theme" of attention surrounded by a structured periphery of inattentional contents. As the field evolves, its contents change their status, sometimes smoothly, sometimes abruptly. Inner thoughts, a sense of one's body, and the physical environment are dominant field contents. These ideas can be linked with (and help unify) contemporary theories about the neural correlates of consciousness, inattention, the small world structure of the brain, meta-stable dynamics, embodied cognition, and predictive coding in the brain. Published by Elsevier Inc.
In this paper, a quantization scheme for haptic data compression is proposed and the results of its application to a motion copying system are described. The data compression of image and audio data has been researche...
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In this paper, a quantization scheme for haptic data compression is proposed and the results of its application to a motion copying system are described. The data compression of image and audio data has been researched a lot. However, the data compression of haptic data has not been much researched yet although the amount of haptic data is large in general. The discrete cosine transform (DCT) is used for the lossy compression of haptic data in this paper. A new quantization scheme using characteristics of DCT is proposed. By using a proposed method, a larger signal-to-noise ratio compared to conventional method is achieved. In the experiment, the validity of the proposed method was verified.
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