The Arimoto-Blahut algorithm determines the capacity of a discrete memoryless channel through an iterative process in which the input probability distribution is adapted at each iteration. While it converges towards t...
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The Arimoto-Blahut algorithm determines the capacity of a discrete memoryless channel through an iterative process in which the input probability distribution is adapted at each iteration. While it converges towards the capacity-achieving distribution for any discrete memoryless channel, the convergence can be slow when the channel has a large input alphabet. This is unfortunate when only a small number of the input letters are assigned non-zero probabilities in the capacity-achieving distribution. If we knew which input letters will end up with a probability of zero, we could eliminate these letters and operate the algorithm on a subset of the input alphabet. The algorithm would converge towards the same solution faster. We present an algorithm which makes use of this fact to speed up the convergence of the Arimoto-Blahut algorithm in such situations.
We develop a multi-channel channel-optimized scheme for embedded zerotree wavelet (EZW) image compression in a noisy transmission environment. A block-based modification for EZW is applied to improve the robustness of...
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ISBN:
(纸本)0780362977
We develop a multi-channel channel-optimized scheme for embedded zerotree wavelet (EZW) image compression in a noisy transmission environment. A block-based modification for EZW is applied to improve the robustness of EZW, and to produce several coded bitstreams for transmission over multiple channels with different noise conditions. Then the respective channel noise is considered in the rate-distortion analysis, and the resultant rate-distortion functions are used for optimal bit allocation among the coded bitstreams. The case of burst noise is analyzed as an example.
A channel optimized vector quantizer (COVQ) is studied for the case of transmission over waveform channels. In this work, a number of modulation schemes with multidimensional signal constellations are considered, spec...
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A channel optimized vector quantizer (COVQ) is studied for the case of transmission over waveform channels. In this work, a number of modulation schemes with multidimensional signal constellations are considered, specifically, results on the binary signalling. M-ary phase-shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) performance using COVQ with hard-decision decoding, is optimized for additive white Gaussian noise (AWGN) and flat-fading Rayleigh channel. In addition, when a flat-fading Rayleigh channel is assumed, diversity techniques are used and evaluated to improve the performance of the system.
An algorithm to extract the semantic video object in the first frame of a video sequence is proposed. The extracted closed object contour provides an accurate video object model for automatic object extraction in the ...
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An algorithm to extract the semantic video object in the first frame of a video sequence is proposed. The extracted closed object contour provides an accurate video object model for automatic object extraction in the following frames. Two polygons are input by the user to specify the area in which the object boundary is located. Based on this, edges which belong to the video object of interest are selected by a local object contour model. Then an active contour model (snake) is applied to refine the closed object contour.
This work combines a new fast context-search algorithm with the lossless source coding models of PPM to achieve a lossless data compression algorithm with the linear context-search complexity and memory of BWT and Ziv...
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This work combines a new fast context-search algorithm with the lossless source coding models of PPM to achieve a lossless data compression algorithm with the linear context-search complexity and memory of BWT and Ziv-Lempel codes and the compression performance of PPM-based algorithms. Both sequential and nonsequential encoding are considered. The proposed algorithm yields an average rate of 2.27 bits per character (bpc) on the Calgary corpus, comparing favorably to the 2.33 and 2.34 bpc of PPM5 and PPM/sup */ and the 2.43 bpc of BW94 but not matching the 2.12 bpc of PPMZ9, which, at the time of this publication, gives the greatest compression of all algorithms reported on the Calgary corpus results page. The proposed algorithm gives an average rate of 2.14 bpc on the Canterbury corpus. The Canterbury corpus Web page gives average rates of 1.99 bpc for PPMZ9, 2.11 bpc for PPM5, 2.15 bpc for PPM7, and 2.23 bpc for BZIP2 (a BWT-based code) on the same data set.
The problem of source coding with multiple descriptions (the multiple descriptions problem) was first posed by Gersho, Witsenhausen, Wolf, Wyner, Ziv and Ozarow at the 1979 IEEE Information Theory Workshop. Since then...
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The problem of source coding with multiple descriptions (the multiple descriptions problem) was first posed by Gersho, Witsenhausen, Wolf, Wyner, Ziv and Ozarow at the 1979 IEEE Information Theory Workshop. Since then, this problem has been extensively studied. El Gamal and Cover (1982) obtained an inner bound on the rate-distortion region for multiple descriptions, and showed that it is tight for the case of deterministic distortion measures. Ozarow (1980) showed that this inner bound is also tight for the Gaussian source with the square error distortion. Furthermore, Ahlswede (1985), Zhang and Berger (1987) showed that the El Gamal-Cover region is tight for the case of no excess rate for the joint description. In the excess rate case, Zhang and Berger showed by a counterexample that the El Gamal-Cover region is not tight in general. How to establish the rate-distortion region for multiple descriptions is still open. It is one of the well known hard problems in multiuser information theory. In this paper, we study the problem of source coding with multiple descriptions for a discrete memoryless source.
Highly efficient and robust source coding, channel coding, and packetization techniques are proposed for video streaming over wireless ATM. Video received from the wireline source is adapted at the base station to bot...
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Highly efficient and robust source coding, channel coding, and packetization techniques are proposed for video streaming over wireless ATM. Video received from the wireline source is adapted at the base station to both the hardware capabilities of mobile host and the time-varying wireless channel conditions. For wireless transmission, source coding, channel coding, and packetization are jointly implemented as part of the application. For source coding, we propose to use our three-dimensional significance-linked connected component analysis video codec. For channel coding and packetization, both intracell and interlaced (intercell) forward error correction are applied. Furthermore, the time-varying channel characteristics is exploited by adaptively allocating the total bit budget between source coding and channel coding. Extensive performance evaluation demonstrates the effectiveness of the proposed wireless video streaming technique.
Consider a pair of random variables (X,Y) with distribution P. The probability rank function is defined so that G(x|y)=1 for the most probable outcome x conditional on Y=y, G(x|y)=2 for the second most probable outcom...
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Consider a pair of random variables (X,Y) with distribution P. The probability rank function is defined so that G(x|y)=1 for the most probable outcome x conditional on Y=y, G(x|y)=2 for the second most probable outcome, and so on, resolving ties between elements with equal probabilities arbitrarily. The function G was considered in Arikan (1996) in the context of finding the unknown outcome of a random experiment by asking questions of the form 'is the outcome equal to x?' sequentially until the actual outcome is determined. The primary focus in Arakan (1996) and Arakan and Merhav (1998) was to find tight bounds on the moments E[G(X|Y)/sup /spl theta//]. The present work is closely related to these works but focuses more directly on the large deviations properties of the probability rank function.
We consider the problem of finding the quantizer Q that quantizes the K-dimensional causal context C/sub i/=(X/sub i-t(i)/,X/sub i-t(2)/,...,X/sub i-t(K)/) of a source symbol X/sub i/ into one of M conditioning states...
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We consider the problem of finding the quantizer Q that quantizes the K-dimensional causal context C/sub i/=(X/sub i-t(i)/,X/sub i-t(2)/,...,X/sub i-t(K)/) of a source symbol X/sub i/ into one of M conditioning states such that the conditional entropy H(X/sub i/|Q(C/sub i/)) is minimized. The resulting minimum conditional entropy context quantizer can be used for sequential coding of the sequence X/sub 0/, X/sub 1/, X/sub 2/,.
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