A lower bound for the rate-distortion function of spike sources that is asymptotically tight

Palzer, Lars; Timo, Roy

doi:10.1109/itw.2016.7606804

Cited by 4 publications

(8 citation statements)

References 11 publications

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“…Although the lower bound on the ODR of CS in [46] does not account for the rate budget required to convey the support information to the decoder, the proposed schemes are superior for R > 1.2 bits/sample. It is noted also that the lower and the upper bounds on the IDR for BG in [18] and [17] are tight for high rates.…”

Section: A Performance Analysismentioning

confidence: 93%

“…1) Information distortion-rate (IDR) of sparse sources: Several works address the problem of deriving information theoretic bounds in terms of the IDR for directly observed sparse sources, i.e., when x is known at the source encoder. In particular, upper and lower bounds on the IDR for a class of sparse signals (i.e., Bernoulli-Gaussian (BG) sources) are provided in [5], [17], [18]. These bounds are asymptotically tight in the high-rate low-distortion region.…”

Section: Introductionmentioning

confidence: 99%

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Lossy Compression of Noisy Sparse Sources Based on Syndrome Encoding

Elzanaty

Giorgetti

Chiani

2019

IEEE Trans. Commun.

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Data originating from devices and sensors in Internet of Things scenarios can often be modeled as sparse signals. In this paper, we provide new source compression schemes for noisy sparse and non-strictly sparse sources, based on channel coding theory. Specifically, nonlinear excision filtering by means of model order selection or thresholding is first used to detect the support of the non-zero elements of sparse vectors in noise. Then, the sparse sources are quantized and compressed using syndrome based encoders. The theoretical performance of the schemes is provided, accounting for the uncertainty in the support estimation. In particular, we derive the operational distortionrate and operational distortion-energy of the encoders for noisy Bernoulli-uniform and Bernoulli-Gaussian sparse sources. It is found that the performance of the proposed encoders approaches the information-theoretic bounds for sources with low sparsity order. As a case study, the proposed encoders are used to compress signals gathered from a real wireless sensor network for environmental monitoring.

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Section: A Performance Analysismentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Lossy Compression of Noisy Sparse Sources Based on Syndrome Encoding

Elzanaty

Giorgetti

Chiani

2019

IEEE Trans. Commun.

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“…In Section III, we derive an analytically tractable lower bound to R rem X (D), whereas in Section IV we develop a method to numerically approximate R rem X (D). Note that the difficulty resides also in the direct compression of X for which only RD bounds have been derived [51]- [53].…”

Section: Remote Rdfmentioning

confidence: 99%

“…R rem X|B (D) reflects the remote sensing nature of the lossy CS: regardless of the rate, the lowest achievable distortion is ultimately dictated by D Z|B -the constant term solely governed by the noisy measurement model in (16). This unavoidable compression performance degradation caused by the indirect observations of the source distinguishes the lossy CS from directly compressing X; RD bounds for compressing sparse sources have been derived in, e.g., [51]- [53]. Note that a constant distortion floor occurs whether or not the support SI is available -only the respective levels for R rem X|B (D) and R rem X (D) are different.…”

Section: Characterization Of the Conditional Remote Rdfmentioning

confidence: 99%

“…Empirical performance of QCS algorithms that optimize either the encoder or decoder can be found in, e.g., [25]- [27], [38], [45], [46]; joint optimization of encoderdecoder pair(s) was studied in [41], [47]- [50]. RD bounds for directly compressing sparse sources were derived in [51]- [53], and the compression of (sparse) Bernoulli generalized Gaussian sources via uniform SQ was addressed in [54].…”

Section: Introductionmentioning

confidence: 99%

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Rate-Distortion Performance of Lossy Compressed Sensing of Sparse Sources

Leinonen¹,

Codreanu²,

Juntti³

et al. 2018

IEEE Trans. Commun.

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We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a sensor observes a sparse information source indirectly. The compressed noisy measurements are communicated to the decoder for signal reconstruction with the aim to minimize the mean square error distortion. An analytically tractable lower bound to the remote rate-distortion function (RDF), i.e., the conditional remote RDF, is derived by providing support side information to the encoder and decoder. For this setup, the best encoder separates into an estimation step and a transmission step. A variant of the Blahut-Arimoto algorithm is developed to numerically approximate the remote RDF. Furthermore, a novel entropy coding based quantized CS method is proposed. Numerical results illustrate the main rate-distortion characteristics of the lossy CS, and compare the performance of practical quantized CS methods against the proposed limits.Index Terms-Remote source coding, conditional ratedistortion theory, side information, Blahut-Arimoto algorithm, wireless sensor networks.

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RETRACTED ARTICLE: Huffman quantization approach for optimized EEG signal compression with transformation technique

Rajasekar

Pushpalatha

2020

Soft Comput

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A lower bound for the rate-distortion function of spike sources that is asymptotically tight

Cited by 4 publications

References 11 publications

Lossy Compression of Noisy Sparse Sources Based on Syndrome Encoding

Lossy Compression of Noisy Sparse Sources Based on Syndrome Encoding

Rate-Distortion Performance of Lossy Compressed Sensing of Sparse Sources

RETRACTED ARTICLE: Huffman quantization approach for optimized EEG signal compression with transformation technique

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