This paper shows new general nonasymptotic achievability and converse bounds and performs their dispersion analysis for the lossy compression problem in which the compressor observes the source through a noisy channel...
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This paper shows new general nonasymptotic achievability and converse bounds and performs their dispersion analysis for the lossy compression problem in which the compressor observes the source through a noisy channel. While this problem is asymptotically equivalent to a noiseless lossy sourcecoding problem with a modified distortion function, nonasymptotically there is a noticeable gap in how fast their minimum achievable coding rates approach the common rate-distortion function, as evidenced both by the refined asymptotic analysis (dispersion) and the numerical results. The size of the gap between the dispersions of the noisy problem and the asymptotically equivalent noiseless problem depends on the stochastic variability of the channel through which the compressor observes the source.
Video shared over the internet is commonly referred to as user generated content (UGC). UGC video may have low quality due to various factors including previous compression. UGC video is uploaded by users, and then it...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
Video shared over the internet is commonly referred to as user generated content (UGC). UGC video may have low quality due to various factors including previous compression. UGC video is uploaded by users, and then it is re-encoded to be made available at various levels of quality. In a traditional video coding pipeline the encoder parameters are optimized to minimize a rate-distortion criterion, but when the input signal has low quality, this results in sub-optimal coding parameters optimized to preserve undesirable artifacts. In this paper we formulate the UGC compression problem as that of compression of a noisy/corrupted source. The noisy source coding theorem reveals that an optimal UGC compression system is comprised of optimal denoising of the UGC signal, followed by compression of the denoised signal. Since optimal denoising is unattainable and users may be against modification of their content, we propose encoding the UGC signal, and using denoised references only to compute distortion, so the encoding process can be guided towards perceptually better solutions. We demonstrate the effectiveness of the proposed strategy for JPEG compression of UGC images and videos.
This paper shows new general nonasymptotic achievability and converse bounds and performs their dispersion analysis for the lossy compression problem in which the compressor observes the source through a noisy channel...
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ISBN:
(纸本)9781479913213
This paper shows new general nonasymptotic achievability and converse bounds and performs their dispersion analysis for the lossy compression problem in which the compressor observes the source through a noisy channel. While this problem is asymptotically equivalent to a noiseless lossy sourcecoding problem with a modified distortion function, nonasymptotically there is a difference in how fast their minimum achievable coding rates approach the rate-distortion function, providing yet another example where at finite blocklengths one must put aside traditional asymptotic thinking.
We present a characterization of the Gaussian CEO rate region, in which the operational point at each boundary is characterized by one free parameter. That parameter determines the water level. Only those sensors whos...
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ISBN:
(纸本)9781538669006
We present a characterization of the Gaussian CEO rate region, in which the operational point at each boundary is characterized by one free parameter. That parameter determines the water level. Only those sensors whose observation noise is below that water level need to compress and transmit their data. Using that characterization, we present a simple (suboptimal) achievable region, expressed in terms of the difference between the noisy and the noiseless rate-distortion functions. Using that achievable region, we can explicitly bound the rate loss due to lack of cooperation among the compressors.
We extend high-rate quantization theory to Wyner-Ziv coding, i.e., lossy sourcecoding with side information at the decoder. Ideal Slepian-Wolf coders are assumed, thus rates are conditional entropies of quantization ...
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We extend high-rate quantization theory to Wyner-Ziv coding, i.e., lossy sourcecoding with side information at the decoder. Ideal Slepian-Wolf coders are assumed, thus rates are conditional entropies of quantization indices given the side information. This theory is applied to the analysis of orthonormal block transforms for Wyner-Ziv coding. A formula for the optimal rate allocation and an approximation to the optimal transform are derived. The case of noisy high-rate quantization and transform coding is included in our study, in which a noisy observation of source data is available at the encoder, but we are interested in estimating the unseen data at the decoder, with the help of side information. We implement a transform-domain Wyner-Ziv video coder that encodes frames independently but decodes them conditionally. Experimental results show that using the discrete cosine transform results in a rate-distortion improvement with respect to the pixel-domain coder. Transform coders of noisy images for different communication constraints are compared. Experimental results show that the noisy Wyner-Ziv transform coder achieves a performance close to the case in which the side information is also available at the encoder. (c) 2006 Elsevier B.V. All rights reserved.
Consider the problem of rate-constrained reconstruction of a finite-alphabet discrete memoryless signal X-n = (X-1,..., X-2), based on a noise-corrupted observation sequence Z(n), which is the finite-alphabet output o...
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Consider the problem of rate-constrained reconstruction of a finite-alphabet discrete memoryless signal X-n = (X-1,..., X-2), based on a noise-corrupted observation sequence Z(n), which is the finite-alphabet output of a discrete memoryless channel (DMC) whose input is X-n. Suppose that there is some uncertainty in the source distribution, in the channel characteristics, or in both. Equivalently, suppose that the distribution of the pairs (X-i, Z(i)), rather than completely being known, is only known to belong to a set Theta. Suppose further that the relevant performance criterion is the probability of excess distortion, i.e., letting (X) over cap (n)(Z(n)) denote the reconstruction, we are interested in the behavior of P-theta (rho(X-n, (X) over cap (n) (Z(n))) > d(theta)), where rho is a (normalized) block distortion induced by a single-letter distortion measure and P-theta denotes the probability measure corresponding to the case where (X-i, Z(i)) similar to theta, theta is an element of Theta. Since typically this probability will either not decay at all or do so at an exponential rate, it is the rate of this decay which we focus on. More concretely, for a given rate R > 0 and a family of distortion levels {d(theta)}(thetais an element ofTheta), we are interested in families of exponential levels {I-theta}(thetais an element ofTheta) which are achievable in the sense that for large n there exist rate-R schemes satisfying -1/n log P-theta (rho(X-n, (X) over cap (n)(Z(n))) > d(theta)) greater than or equal to I-theta, for all theta is an element of Theta. Our main result is a complete "single-letter" characterization of achievable levels {I-theta}(thetais an element ofTheta) perany given triple (Theta, R, {d(theta)}(thetais an element ofTheta)). Equipped with this result, we later turn to addressing the question of the "right" choice of {I-theta}(thetais an element ofTheta). Relying on methodology recently put forth by Feder and Merhav in the context of the compos
Compression of noisy imagery usually consists of two stages, prefiltering followed by encoding. In this paper we present a technique based on vector quantization, which combines noise reduction and compression into on...
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ISBN:
(纸本)0819453617
Compression of noisy imagery usually consists of two stages, prefiltering followed by encoding. In this paper we present a technique based on vector quantization, which combines noise reduction and compression into one step. The idea is to generate a codebook, consisting only of clean image data, which is then used for quantization of the noisy imagery. Simulations performed shows that this approach can efficiently handle images corrupted by noise, and compared to MPEG-4 encoding, this technique, in spite of its simplicity, is the better choice when dealing with high levels of noise.
Lossy compression of a discrete memoryless source (DMS) with respect to a single-letter distortion measure is considered. We study the best attainable tradeoff between the exponential rates of the probabilities that t...
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Lossy compression of a discrete memoryless source (DMS) with respect to a single-letter distortion measure is considered. We study the best attainable tradeoff between the exponential rates of the probabilities that the codeword length and that the cumulative distortion exceed respective thresholds for two main cases. The first scenario examined is that where the source is corrupted by a discrete memoryless channel (DMC) prior to reaching the coder. In the second part of this work, we examine the universal setting, where the (noise-free) source is an unknown member P-theta of a given family { P-theta, theta is an element of Theta}. Here, inspired by an approach which was proven fruitful recently in the context of composite hypothesis testing, we allow the constraint on the excess-code-length exponent to be theta-dependent. Corollaries are derived for some special cases of interest, including Marton's classical sourcecoding exponent and its generalization to the case where the constraint on the rate of the code is relaxed from an almost sure constraint to a constraint on the excess-code-length exponent.
We continue the study of adaptive schemes for the sequential lossy coding of individual sequences which was recently initiated by Linder and Lugosi. Specifically, we consider fixed-rate lossy coding systems of fixed (...
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We continue the study of adaptive schemes for the sequential lossy coding of individual sequences which was recently initiated by Linder and Lugosi. Specifically, we consider fixed-rate lossy coding systems of fixed (or zero) delay where the encoder (which is allowed to use randomization) and the decoder are connected via a noiseless channel of a given capacity. It is shown that for any finite set of such coding schemes of a given rate, there exists a source code (adhering to the same structural and delay limitations) with the same rate whose distortion is with high probability almost as small as that of the best scheme in that set, uniformly for all individual sequences. Applications of this result to reference classes of special interest are outlined. These include the class of scalar quantizers, trellis encoders with sliding block decoders, and differential pulse code modulator (DPCM)-based source codes. In particular, for the class of all scalar quantizers, a source code is obtained with (normalized) distortion redundancy relative to the best scheme in the reference class of order n(-1/3) log n (where n is the sequence length). This improves the n(-1/5) log n rate achieved by Linder and Lugosi. More importantly, the decoder here is deterministic and, in particular, does not assume a common randomization sequence available at both encoder and decoder. Finally, we consider the case where the individual sequence is corrupted by noise prior to reaching the coding system, whose goal now is to reconstruct a sequence with small distortion relative to the clean individual sequence. It is shown that for the case of a finite alphabet and an invertible channel transition probability matrix, for any finite set of sliding-window schemes of a given rate, there exists a source code (allowed to use randomization yet adhering to the same delay constraints) whose performance is, with high probability, essentially as good as the best scheme in the class, for all individual sequenc
Compression of a noisysource is usually a two stage problem, involving the operations of estimation (denoising) and quantization. A survey of literature on this problem reveals that for the squared error distortion m...
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ISBN:
(纸本)0819429155
Compression of a noisysource is usually a two stage problem, involving the operations of estimation (denoising) and quantization. A survey of literature on this problem reveals that for the squared error distortion measure, the best possible compression strategy is to subject the noisysource to an optimal estimator followed by an optimal quantizer for the estimate. What we present in this paper is a simple but sub-optimal vector quantization (VQ) strategy that combines estimation and compression in one efficient step. The idea is to train a VQ on pairs of noisy and clean images. When presented with a noisy image, our VQ-based system estimates the noise variance and then performs joint denoising and compression. Simulations performed on images corrupted by additive, white, Gaussian noise (AWGN) show significant denoising at various bit rates. Results also indicate that our system is robust enough to handle a wide range of noise variances, while designed for a particular noise variance.
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