We consider problems where one wishes to represent a parameter associated with a signal source – subject to a certain rate and distortion – based on the observation of a number of realizations of the source signal. By reducing these indirect vector quantization problems to a standard vector quantization one, we provide a bound to the fundamental interplay between the rate and distortion in the large-rate setting. We specialize this characterization to two particular quantization scenarios: i) the representation of the mean of a multivariate Gaussian source; and ii) the representation of the eigen-spectrum of a multivariate Gaussian source. Numerical results compare our quantization approach to an approach where one recovers the parameters from the representation of the source signals itself: in addition to revealing that the characterization is sharp in the large-rate setting, the results also show that our approach offers considerable gains.
Rodrigues, M, Deligiannis, N, Lai, L & Eldar, Y 2017, Rate-distortion trade-offs in acquisition of signal parameters. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings: ICASSP 2017., 7953329, pp. 6105-6109, IEEE International Conference on Acoustics, Speech, and Signal Processing, New Orleans, United States, 5/03/17. https://doi.org/10.1109/ICASSP.2017.7953329
Rodrigues, M., Deligiannis, N., Lai, L., & Eldar, Y. (2017). Rate-distortion trade-offs in acquisition of signal parameters. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings: ICASSP 2017 (pp. 6105-6109). Article 7953329 https://doi.org/10.1109/ICASSP.2017.7953329
@inproceedings{42e8d393eaf84bb785639426ecdf5669,
title = "Rate-distortion trade-offs in acquisition of signal parameters",
abstract = "We consider problems where one wishes to represent a parameter associated with a signal source – subject to a certain rate and distortion – based on the observation of a number of realizations of the source signal. By reducing these indirect vector quantization problems to a standard vector quantization one, we provide a bound to the fundamental interplay between the rate and distortion in the large-rate setting. We specialize this characterization to two particular quantization scenarios: i) the representation of the mean of a multivariate Gaussian source; and ii) the representation of the eigen-spectrum of a multivariate Gaussian source. Numerical results compare our quantization approach to an approach where one recovers the parameters from the representation of the source signals itself: in addition to revealing that the characterization is sharp in the large-rate setting, the results also show that our approach offers considerable gains.",
keywords = "Rate-Distortion, Signal Acquisition, Signal Parameters Acquisition, Vector Quantization",
author = "Miguel Rodrigues and Nikolaos Deligiannis and Lifeng Lai and Yonina Eldar",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953329",
language = "English",
pages = "6105--6109",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
note = "IEEE International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 05-03-2017 Through 09-03-2017",
url = "http://www.ieee-icassp2017.org",
}