This paper proposes a new construction of rate-adaptive coding schemes based on Low Density Parity Check (LDPC) codes for Slepian Wolf source coding. Unlike standard rate-adaptive source coding schemes, the code const...
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ISBN:
(纸本)9781538623213
This paper proposes a new construction of rate-adaptive coding schemes based on Low Density Parity Check (LDPC) codes for Slepian Wolf source coding. Unlike standard rate-adaptive source coding schemes, the code construction we propose is based on finite-length code design tools that permit to greatly improve the decoding performance at short to moderate length. In particular, our method permits to limit the number of short cycles in the codes at all rates of interest, and to avoid eliminating some source bits from the code constraints. The proposed method shows a performance improvement of up to an order of magnitude at almost all the considered rates compared to the standard LDPCA construction.
We consider a fixed-rate zero-delay source coding problem where a stationary vector-valued Gauss-Markov source is compressed subject to an average mean-squared error (MSE) distortion constraint. We address the problem...
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ISBN:
(纸本)9781538648834
We consider a fixed-rate zero-delay source coding problem where a stationary vector-valued Gauss-Markov source is compressed subject to an average mean-squared error (MSE) distortion constraint. We address the problem by considering the Gaussian nonanticipative rate distortion function (NRDF) which is a lower bound to the zero-delay Gaussian RDF. Then, we use its corresponding optimal "test-channel" to characterize the stationary Gaussian NRDF and evaluate the corresponding information rates. We show that the Gaussian NRDF can be achieved by p-parallel fixed-rate scalar uniform quantizers of finite support with dithering signal up to a multiplicative distortion factor and a constant rate penalty. We demonstrate our framework with a numerical example.
This paper revisits the quadratic Gaussian two-encoder source-coding problem, for which a Gaussian quantizeand-bin scheme, also known as the Berger-Tung scheme, is known to achieve the entire rate region. We present a...
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ISBN:
(纸本)9781538692912
This paper revisits the quadratic Gaussian two-encoder source-coding problem, for which a Gaussian quantizeand-bin scheme, also known as the Berger-Tung scheme, is known to achieve the entire rate region. We present a new proof of the impossibility half of the rate-region optimality result that is arguably more direct.
A highly reliable and efficient wireless communication system for compressed images delivery is required in emerging multimedia applications. However, the transmission of the image can be attenuated by different facto...
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ISBN:
(纸本)9781728140643
A highly reliable and efficient wireless communication system for compressed images delivery is required in emerging multimedia applications. However, the transmission of the image can be attenuated by different factors mainly involved by the wireless communication multimedia systems. In the past decades, various source and channel coding schemes have been developed separately. In this paper, we investigate a joint source-channel coding scheme to improve the image transmission performance. Our main target is to reach a good tradeoff in terms of rate-distortion for Set Partitioning in Hierarchical Trees (SPIHT) image transmission. For the wireless communication part, we consider the Audio Visual PHY Ultra Wide Band IEEE 802.15.3c system. The proposed strategies rely on adaptive process applied for error protection and the modulation operations. Thus, error correcting codes for unequal error protection (UEP) of the compressed bit streams is included. Then, Hierarchical Modulation (HM) is applied to the different bits according to their importance levels. Full of simulations are performed and our results show image quality improvements at the receiver using multilevel UEP and Hierarchical Modulation compared to the ordinary joint source-channel coding system without introducing any extra bandwidth usage.
Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly...
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Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at least as large as the observed alphabet. In this paper, we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the binary independent component analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression, and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases.
A source coding (quantization) scheme is studied for the feedback of discrete-time and continuous-state cyber-physical systems (CPSs). It is formulated as a sequential coding optimization problem. The goal is to find ...
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A source coding (quantization) scheme is studied for the feedback of discrete-time and continuous-state cyber-physical systems (CPSs). It is formulated as a sequential coding optimization problem. The goal is to find a deterministic but adaptive policy, as a series of mappings from the historical information to the quantization strategy. In particular, an optimization problem is formulated, and then solved by the Bellman equation in dynamic programming (DP). To overcome the challenge of continuous state space, a practical solution is proposed by leveraging the approximate DP (ADP). The performance of the proposed strategy is examined for both scalar and vector dynamical systems in two practical applications. It shows that the designed policy can significantly outperform the simple fixed quantization strategies in CPSs and can be applied to the mobile/vehicle communication.
This paper investigates source compression for Indonesian local languanges and derives a closed-form expression of their outage probabilities using Slepian-Wolf coding theorem to observe their potential applications f...
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ISBN:
(纸本)9781538627785
This paper investigates source compression for Indonesian local languanges and derives a closed-form expression of their outage probabilities using Slepian-Wolf coding theorem to observe their potential applications for fifth generation (5G) services. The 5G services involving language compressions are, for example, hologram-based telephone services and voice command of self-driving car. We also propose labelling pattern for Indonesian local languages based on Huffman codes having expected length closes to the entropies of Sundanese, Javanese, and Balinese. To observe the compression level of Indonesian local languages, we also provide a comparison analysis of the outage probability to French and English as some representative examples of foreign languages. We found that Indonesian local languages have better compression rates and lower outage probability and are potential for future 5G applications involving language services.
Lossy source coding under the mean-squared error fidelity criterion is considered. The rate-distortion function can be expressed in closed form only for very special cases, including Gaussian sources. The classical up...
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Lossy source coding under the mean-squared error fidelity criterion is considered. The rate-distortion function can be expressed in closed form only for very special cases, including Gaussian sources. The classical upper and lower bounds look exactly alike, except that the upper bound has the source power (variance) whereas the lower bound has the source entropy-power. This pleasing duality of power and entropy-power extends to the case of remote source coding, i.e., the case where the encoder only gets to observe the source through a noisy channel. Bounds are presented both for the centralized and for the distributed case, often referred to as the CEO problem.
We introduce a new class of zero-delay source coding problems where a vector-valued Gauss-Markov source is conveyed subject to covariance matrix distortion constraints. We address this problem by defining an informati...
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We introduce a new class of zero-delay source coding problems where a vector-valued Gauss-Markov source is conveyed subject to covariance matrix distortion constraints. We address this problem by defining an information theoretic measure where we minimize mutual information subject to causality constraints and covariance matrix distortion constraints. The resulting measure serves as a lower bound to the zero-delay rate distortion function (RDF). We solve this problem by showing that it is semidefinite representable and, thus, can be computed numerically. We also show that for this new class of information measures, it is possible to have achievable rates up to a constant space-filling loss due to a vector lattice quantizer and a constant loss due to entropy coding. We corroborate our framework with illustrative simulation examples.
In this paper, we consider a source coding with side information partially used at the decoder through a codeword. We assume that there exists a relative delay (or gap) of the correlation between the source sequence a...
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ISBN:
(纸本)9781538682234;9784885523182
In this paper, we consider a source coding with side information partially used at the decoder through a codeword. We assume that there exists a relative delay (or gap) of the correlation between the source sequence and side information. We also assume that the delay is unknown but the maximum of possible delays is known to two encoders and the decoder, where we allow the maximum of delays to be subject to change by the block length. In this source coding, we give an inner bound and an outer bound on the achievable rate region, where the achievable rate region is the set of rate pairs of encoders such that the decoding error probability vanishes as the block length tends to infinity. Furthermore, we clarify that the inner bound coincides with the outer bound when the maximum of delays for the block length converges to a constant.
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