The representational capacity and inherent function of any neuron, neuronal population or cortical area in the brain is dynamic and context-sensitive. Functional integration, or interactions among brain systems, that ...
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The representational capacity and inherent function of any neuron, neuronal population or cortical area in the brain is dynamic and context-sensitive. Functional integration, or interactions among brain systems, that employ driving (bottom up) and backward (top-down) connections, mediate this adaptive and contextual specialisation. A critical consequence is that neuronal responses, in any given cortical area, can represent different things at different times. This can have fundamental implications for the design of brain imaging experiments and the interpretation of their results. Our arguments are developed under generative models of brain function, where higher-level systems provide a prediction of the inputs to lower-level regions. Conflict between the two is resolved by changes in the higher-level representations, which are driven by the ensuing error in lower regions, until the mismatch is "cancelled". From this perspective the specialisation of any region is determined both by bottom-up driving inputs and by top-down predictions. Specialisation is therefore not an intrinsic property of any region but depends on both forward and backward connections with other areas. Because the latter have access to the context in which the inputs are generated they are in a position to modulate the selectivity or specialisation of lower areas. The implications for classical models (e.g., classical receptive fields in electrophysiology, classical specialisation in neuroimaging and connectionism in cognitive models) are severe and suggest these models may provide incomplete accounts of real brain architectures. Here we focus on the implications for cognitive neuroscience in the context of neuroimaging.
Scalable video coders have traditionally avoided using enhancement-layer (EL) information to predict the base layer (BL), so as to avoid so-called "drift". As a result, they are less efficient than a one-lay...
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Scalable video coders have traditionally avoided using enhancement-layer (EL) information to predict the base layer (BL), so as to avoid so-called "drift". As a result, they are less efficient than a one-layer coder. Fine granularity scalable (FGS) coders avoid using EL information to predict the EL as well, suffering even further inefficiencies. In this paper, we explore a scalable video coder that allows drift, by predicting the BL from EL information. However, we show that through careful management of the amount of drift introduced, the video quality at low rates is only marginally worse than the drift-free case, while the overall compression efficiency is not much worse than a one-layer encoder.
When compressed video is transmitted over erasure-prone channels, errors will propagate whenever temporal or spatial prediction is used. Typical tools to combat this error propagation are packetization, re-synchronizi...
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ISBN:
(纸本)0769510310
When compressed video is transmitted over erasure-prone channels, errors will propagate whenever temporal or spatial prediction is used. Typical tools to combat this error propagation are packetization, re-synchronizing codewords, intra-coding, and scalability. In recent years, the concern over so-called "drift" has sent researchers toward structures for scalability that do not use enhancement-layer information to predict base-layer information and hence have no drift. In this paper, we propose alternative structures for scalability that use previous enhancement-layer information to predict the current base layer, while simultaneously managing the resulting possibility of drift. These structures allow better compression efficiency, while introducing only limited impairments in the quality of the reconstruction.
An efficient method for quantising the magnitude of the LP-residual spectrum is proposed for high quality sinusoidal speech coding. It exploits the intra-frame perceptual preference of the magnitudes using mel-scale-b...
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An efficient method for quantising the magnitude of the LP-residual spectrum is proposed for high quality sinusoidal speech coding. It exploits the intra-frame perceptual preference of the magnitudes using mel-scale-based warping, and the inter-frame statistical correlation of the successive magnitude vectors using predictive coding. A switched quantiser between memoryless and predictive coding schemes is also introduced for the resilience against channel errors while producing performances, in terms of weighted signal to noise ratio, superior to a memoryless quantiser and comparable to a predictive quantiser. A subjective speech quality test shows its suitability in developing high-quality sinusoidal speech coders.
The design of predictive quantizers generally suffers from difficulties due to the prediction loop, which have an impact on the convergence and the stability of the design procedure. We previously proposed an asymptot...
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ISBN:
(纸本)0769510310
The design of predictive quantizers generally suffers from difficulties due to the prediction loop, which have an impact on the convergence and the stability of the design procedure. We previously proposed an asymptotically closed-loop approach to quantizer design for predictive coding applications, which benefits from the stability of open-loop design while asymptotically optimizing the actual closed-loop system. In this paper, we present an enhancement to the approach where joint optimization of both predictor and quantizer is performed within the asymptotically closed-loop framework. The proposed design method is tested on synthetic sources (first-order Gauss and Laplacian-Markov sequences), and on natural sources, in particular, line spectral frequency parameters of speech signals.
Multi-view image coding benefits from knowledge of the depicted scene's 3D geometry. To exploit geometry information for compression, two different approaches can be distinguished. In texture-based coding, images ...
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Multi-view image coding benefits from knowledge of the depicted scene's 3D geometry. To exploit geometry information for compression, two different approaches can be distinguished. In texture-based coding, images are converted to texture maps prior to compression. In image-based predictive coding, geometry is used for disparity compensation and occlusion detection between images. coding performance of both approaches depends on the accuracy of the available geometry model. Texture-based and image-based coding are compared with regard to the influence of geometry accuracy on coding efficiency. The results are theoretically explained. Experiments with natural as well as synthetic image sets show that texture-based coding is more sensitive to small geometry inaccuracies than image-based coding. For approximate geometry models, image-based coding performs best, while texture-based coding yields superior coding results if scene geometry is exactly known.
This paper investigates the encoding of vehicular position information using predictive algorithms in inter-vehicle communications (IVC) from the source- and channel-coding viewpoints. Assuming the 15-mode vehicular d...
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This paper investigates the encoding of vehicular position information using predictive algorithms in inter-vehicle communications (IVC) from the source- and channel-coding viewpoints. Assuming the 15-mode vehicular driving model, three types of schemes are compared: (1) an ordinary pulse-code modulation (PCM) scheme that transmits position information every sampling period, (2) predictive coding schemes, and (3) a novel scheme using predicted information. This paper estimates the decoded errors caused by transmission errors, when position information obtained from a positioning system is transmitted. Simulation results show that the novel scheme is effective as a coding scheme in IVC.
We present a new algorithm for lossless compression of bilevel images. The algorithm represents the image to be compressed with a sequential logic function and then, minimizes it to obtain a more compact representatio...
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We present a new algorithm for lossless compression of bilevel images. The algorithm represents the image to be compressed with a sequential logic function and then, minimizes it to obtain a more compact representation. As the minimization of Boolean functions requires a high computational cost, our algorithm represents them with ordered binary-decision diagrams (OBDD), which can be simplified with a low computational cost. Once the OBDD of the sequential logic function has been obtained and reduced, it is coded efficiently in order to reduce the redundancy present on it. Arithmetic coding is also used to remove even more the residual redundancy. The results obtained show significant improvements with respect to previous works.
This paper proposes a new framework for the construction of motion compensated wavelet transforms, with application to efficient highly scalable video compression. Motion compensated transform techniques, as distinct ...
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This paper proposes a new framework for the construction of motion compensated wavelet transforms, with application to efficient highly scalable video compression. Motion compensated transform techniques, as distinct from motion compensated predictive coding, represent a key tool in the development of highly scalable video compression algorithms. The proposed framework overcomes a variety of limitations exhibited by existing approaches. This new method overcomes the failure of frame warping techniques to preserve perfect reconstruction when tracking complex scene motion. It also overcomes some of the limitations of block displacement methods. Specifically, the lifting framework allows the transform to exploit inter-frame redundancy without any dependence on the model selected for estimating and representing motion. A preliminary implementation of the proposed approach was tested in the context of a scalable video compression system, yielding PSNR performance competitive with other results reported in the literature.
Lossless image coding that can recover original image from its compressed signal is required in the fields of medical imaging, fine arts, printing, and any applications demanding high image fidelity. MAR (Multiplicati...
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ISBN:
(纸本)0819431249
Lossless image coding that can recover original image from its compressed signal is required in the fields of medical imaging, fine arts, printing, and any applications demanding high image fidelity. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. In this method, prediction coefficients are fixed within the subdivided block-by-block image and cannot to be adopted to local statistics efficiently. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder at each block. In this paper, we propose an improved MAR coding method based on image segmentation. The proposed MAR predictor can be adapted to local statistics of image efficiently. This coding method does not need transmit side-information to the decoder at each pixel. The effectiveness of the proposed model is shown through experiments using SHD images.
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