We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integ...
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We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation, the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate versus distortion performance is comparable to those of the most efficient lossy compression methods.
Linear predictive coding (LPC) analysis of speech is made using a stationary model while parts of speech such as stop consonants are highly nonstationary. An asymptotic analysis is made of the stability of the LPC mod...
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Linear predictive coding (LPC) analysis of speech is made using a stationary model while parts of speech such as stop consonants are highly nonstationary. An asymptotic analysis is made of the stability of the LPC model obtained from a simplified model of a nonstationary waveform. This model is used to predict the occurrence of unstable LPC models in the analysis of a stop consonant.< >
The human ability to anticipate the consequences that result from action is an essential budding block for cognitive, emotional, and social functioning. A dominant view is that this faculty is based on motor predictio...
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The human ability to anticipate the consequences that result from action is an essential budding block for cognitive, emotional, and social functioning. A dominant view is that this faculty is based on motor predictions, in which a forward model uses a copy of the motor command to predict imminent sensory action-consequences. Although this account was originally conceived to explain the processing of action-outcomes that are tightly coupled to bodily movements, it has been increasingly extrapolated to effects beyond the body. Here, we critically evaluate this generalization and argue that, although there is ample evidence for the role of predictions in the processing of environment-related action-outcomes, there is hitherto little reason to assume that these predictions result from motor-based forward models.
The problem of distortionless encoding when the parameters of the probabilistic model of a source are unknown is considered from a statistical decision theory point of view. A class of predictive and nonpredictive cod...
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The problem of distortionless encoding when the parameters of the probabilistic model of a source are unknown is considered from a statistical decision theory point of view. A class of predictive and nonpredictive codes is proposed that are optimal within this framework. Specifically, it is shown that the codeword length of the proposed predictive code coincides with that of the proposed nonpredictive code for any source sequence. A bound for the redundancy for universal coding is given in terms of the supremum of the Bayes risk. If this supremum exists, then there exists a minimax code whose mean code length approaches it in the proposed class of codes, and the minimax code is given by the Bayes solution relative to the prior distribution of the source parameters that maximizes the Bayes risk.
A deterministic least squares (LS) predictive-transform (PT) multichannel modeling framework is presented. In a manner analogous to the development of minimum mean square error (MSE) PT, the LS PT signal model is obta...
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A deterministic least squares (LS) predictive-transform (PT) multichannel modeling framework is presented. In a manner analogous to the development of minimum mean square error (MSE) PT, the LS PT signal model is obtained as an inherent byproduct of an optimized predictive-transform signal source ''encoder,'' thereby preserving the direct integration of specific data compression concepts into the basic modeling procedure that have proven very useful in the application of minimum MSE PT to coding, detection, estimation, and control, Fundamental properties of the LS PT signal model are presented, and a recursive least squares (RLS) PT modeling procedure is developed, In addition to subsuming conventional RLS signal modeling as a special case and the presence of an integrated transformation mechanism, RLS PT offers greater flexibility when independent ''fading memory'' weighting of both the first- and second-order sample moments is desired, Sufficient conditions for the convergence of the RLS PT parameters to their minimum MSE PT counterparts are developed. In addition, the zero mean constraint (either deterministic or stochastic) imposed on the LS PT signal model's innovation sequence is shown to provide a mechanism for mitigating the deleterious effects of a singular or near-singular data correlation matrix, In a companion paper, the ES PT modeling procedure is employed to yield an efficient multichannel adaptive clutter whitening filter for a space-time array processor problem encountered in airborne moving target indicator (MTI) phased-array radar.
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.
This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded t...
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This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.
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.
La quantification vectorielle sphérique (qvs)consiste à quantifier séparément la norme d’un vecteur d’une part et son orientationd’autre part. La mise en ?uvre d’un tel quantificateur néce...
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La quantification vectorielle sphérique (qvs)consiste à quantifier séparément la norme d’un vecteur d’une part et son orientationd’autre part. La mise en ?uvre d’un tel quantificateur nécessite la spécification d’un ensemble de vecteurs normés (points sur la sphère unité) qui sont les valeurs arrondies possibles pour l’orientation du vecteur. Une fa?on efficace de construire un quantificateur consiste à considérer le sousensemble sphérique d’un réseau régulier de points. En particulier un algorithme très rapide est donné pour la qvs utilisant les points du réseau de Gosset en 8 dimensions. Un second algorithme (optimal également) est donné pour le réseau de Leech en 24 dimensions. Les performances de tels quantificateurs pour le bruit blanc gaussien sont comparées aux limites prédites par la théorie de l’information. Enfin l’application de ces techniques au codage du résidu de parole est discutée.
In this paper, a novel reversible data hiding scheme which utilizes SMVQ prediction indices to embed secret data is proposed. As a result, it improves Yang's work, which takes advantage of counts of indices in dat...
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In this paper, a novel reversible data hiding scheme which utilizes SMVQ prediction indices to embed secret data is proposed. As a result, it improves Yang's work, which takes advantage of counts of indices in data embedding. Our approach enhances the prediction of SMVQ, resulting in a more concentrated distribution of indices. Based on the distribution of SMVQ-p indices, in the embedding process, three clusters (CL (0), CL (1) and CL (2)) are defined. Indices in CL (0) are employed to embed secret data and can support a high embedding capacity. Indices in cluster CL (1) are employed for supporting a lower bit rate. Indices in cluster CL (2) are utilized for lossless reconstruction. Experimental results indicate that lower bit rate and higher embedding efficiency are obtained after encoding and embedding processes by our approach. Furthermore, experimental results demonstrate that the proposed method outperforms other state-of-the-art reversible data hiding schemes as well.
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