In this paper, we propose a new polygonal mesh geometry coding scheme based on a process of structuring by acquiring surrounding vertices of the polygonal mesh one layer at a time. The structuring process begins by se...
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In this paper, we propose a new polygonal mesh geometry coding scheme based on a process of structuring by acquiring surrounding vertices of the polygonal mesh one layer at a time. The structuring process begins by selecting the start vertex and proceeding by acquiring surrounding vertices of the polygonal mesh. As a result, we obtain a 2-D structured vertex table. Structured geometry data are generated according to the structured vertices and encoded by a multiresolution decomposition and space frequency quantization coding method. In our proposed scheme, the multiresolution decomposition uses the connectivity of the polygonal mesh. In addition, with a space frequency quantization coding scheme, we can reduce redundancies of decomposed coefficients at similar positions in different components of decomposition level. Experimental results show that the proposed scheme gives better coding performance at lower bit-rates than the usual schemes.
This paper investigates the design of a system of predictive vector quantizers for distributed sources with memory, in which linear prediction is used to exploit the source memory, while distributed quantization is us...
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This paper investigates the design of a system of predictive vector quantizers for distributed sources with memory, in which linear prediction is used to exploit the source memory, while distributed quantization is used to exploit the correlation between sources. A training-based algorithm is proposed for jointly designing the predictors, binning functions, and reconstruction codebooks of the given system to match the intra-and inter-source correlations. In order to demonstrate the effectiveness of the algorithm, experimental results obtained by designing both scalar and vector quantizers for a set of distributed Gauss-Markov sources are presented. While the optimality of these designs is unknown, it is shown that they convincingly outperform several other alternatives.
In this paper, we propose a novel, discrete wavelet transform (DWT) domain implementation of our previously proposed, pioneering block-based disparity compensated predictive coding algorithm for stereo image compressi...
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In this paper, we propose a novel, discrete wavelet transform (DWT) domain implementation of our previously proposed, pioneering block-based disparity compensated predictive coding algorithm for stereo image compression. Under the present research context we perform predictive coding in the form of pioneering block search in the sub-band domain. The resulting transform domain predictive error image is subsequently converted to a so-called wavelet-block representation, before being quantized and entropy coded by a JPEG-like CODEC. We show that the proposed novel implementation is able to effectively transfer the inherent advantages of DWT-based image coding technology to efficient stereo image pair compression. At equivalent bit rates, the proposed algorithm achieves peak signal to noise ratio gains of up to 5.5 dB, for reconstructed predicted images, as compared to traditional and state of the art DCT and DWT-based predictive coding algorithms. (C) 2003 Elsevier B.V. All rights reserved.
Current models of cortical computation are based on analog quantities instead of single spikes. This paper extends the predictive coding model (Nature Neurosci. 2(1) (1999) 79) to the level of neural signaling. Neuron...
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Current models of cortical computation are based on analog quantities instead of single spikes. This paper extends the predictive coding model (Nature Neurosci. 2(1) (1999) 79) to the level of neural signaling. Neurons in our model use a mixed strategy to transmit information. Spikes are not only messages of computation, but also carriers of information with analog quantities encoded in their phases. Computation is shared among cells both in time and in space, such that information is signaled probabilistically in a distributed synchronous fashion. Contrary to "noise other than signal" interpretation of irregularity of neural signaling, our model proposes a computational role of such variability. (C) 2004 Published by Elsevier B.V.
Based on the relationship among the peak points and valley points of the probability density function (p.d.f) of a stochastic process, whose p.d.f. may e multimodal. the drift coefficient of its associated diffusion p...
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ISBN:
(纸本)7121002159
Based on the relationship among the peak points and valley points of the probability density function (p.d.f) of a stochastic process, whose p.d.f. may e multimodal. the drift coefficient of its associated diffusion process, the 'shift back to center' property of the Markov chain and the state transitive value of the chain, the paper introduces the algorithm for constructing the approximating model of the Markov chain of an Ito stochastic differential equation (AMMC). The results of simulations demonstrate that the variance of the prediction error of the AMMC is not only far smaller than that of the Burg lattice predictor, but also very close to constant. These properties of the algorithm are beneficial to predictor and predictive coding.
Growing percentage of the world population now uses image and video coding technologies on a regular basis. These technologies are behind the success and quick deployment of services and products such as digital pictu...
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Growing percentage of the world population now uses image and video coding technologies on a regular basis. These technologies are behind the success and quick deployment of services and products such as digital pictures, digital television, DVDs, and Internet video communications. Today's digital video coding paradigm represented by the ITU-T and MPEG standards mainly relies on a hybrid of block- based transform and interframe predictive coding approaches. In this coding framework, the encoder architecture has the task to exploit both the temporal and spatial redundancies present in the video sequence, which is a rather complex exercise. As a consequence, all standard video encoders have a much higher computational complexity than the decoder (typically five to ten times more complex), mainly due to the temporal correlation exploitation tools, notably the motion estimation process. This type of architecture is well-suited for applications where the video is encoded once and decoded many times, i.e., one-to-many topologies, such as broadcasting or video-on-demand, where the cost of the decoder is more critical than the cost of the encoder.
Image sequence prediction is widely used in image compression and transmission schemes such as differential pulse code modulation. In traditional predictive coding, linear predictors are usually adopted for simplicity...
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Image sequence prediction is widely used in image compression and transmission schemes such as differential pulse code modulation. In traditional predictive coding, linear predictors are usually adopted for simplicity. The nonlinear Volterra predictor can be employed as an alternative to linear predictors to compensate for the nonstationary and non-Gaussian nature of image sequences. Although the Volterra predictor avoids the smoothing effects introduced by linear predictors, it generally amplifies noise contamination present in the images. In this letter, we propose a nonlinear polynomial weighted median (PWM) predictor for image sequence. The proposed PWM predictor is more robust to noise, while still retaining the information of higher order statistics of pixel values. Experimental results illustrate that the PWM predictor yields good results in both high and low motion video. It is especially suitable for high motion sequence in noisy case. The proposed scheme can be incorporated in new predictive coding systems.
We describe the simulation of a layered cortex model based on the cortical column as a generic local processor. It simulates the signal flow in the layers I-IV of a set of model columns across three hierarchical corti...
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We describe the simulation of a layered cortex model based on the cortical column as a generic local processor. It simulates the signal flow in the layers I-IV of a set of model columns across three hierarchical cortical areas. It demonstrates the fast formation of an initial stimulus hypothesis, and its subsequent refinement by inter-columnar communication. In this prototype simulation, we implement word recognition from a string of characters. The three cortical areas represent letters, syllables, and words, used as a metaphor for visual stimuli. Focusing on the intra- and inter-columnar dynamics, we show how the different processing subsystems interact in order to switch off expected signals and accomplish symbolic recognition of words, and how representations for new words can be constructed based on old representations (self-reference). (c) 2006 Elsevier B.V. All rights reserved.
In this paper, we propose a mechanism that combines retransmission-based error control with multi-path transport (MPT), to provide different levels of protection to real-time video in ad hoc networks. The mechanism fa...
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In this paper, we propose a mechanism that combines retransmission-based error control with multi-path transport (MPT), to provide different levels of protection to real-time video in ad hoc networks. The mechanism factors in the importance of the retransmitted packets to the reconstructed video quality as well as the end-to-end latency constraints to minimize the overhead and maximize the reconstructed video quality at the receiver. Simulation results show that the proposed retransmission mechanism maintains the video quality under different loss rates and mobility speeds, with less overhead compared to error control methods that depend on controlling the intra-update rate. In addition, the mechanism is shown to be more robust to wireless losses and mobility than schemes that combine layered and multiple description coding with multi-path transport. (c) 2007 Elsevier B.V. All rights reserved.
Low complexity video encoding is desired in many applications requiring real-time encoding of video, particularly for mobile equipment. In order to reduce encoding complexity without much sacrifice in visual quality, ...
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Low complexity video encoding is desired in many applications requiring real-time encoding of video, particularly for mobile equipment. In order to reduce encoding complexity without much sacrifice in visual quality, some of the complexity can typically be shifted to the decoder, or the network. infrastructure can take part in the process by performing some encoding and/or decoding tasks. In this paper a new hybrid video coding scheme is proposed that utilizes the standard motion compensated predictive coding architecture and uses one-bit transform (1BT) representations of video frames to facilitate remote motion estimation, The one-bit transform of each video frame is computed at the encoder using low complexity operations and sent to the decoder or network infrastructure, after entropy encoding, for motion estimation. After motion estimation is carried out remotely, the motion vectors are sent back to the encoder for motion compensated predictive coding. In order to reduce the overhead introduced by one-bit transforms, differential encoding of 1BTs is also investigated. The proposed approach can provide low complexity motion compensated predictive video encoding by shifting the high computational load of motion estimation to the decoder or network infrastructure(1).
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