Welcome to the IKCEST

IEEE Transactions on Information Theory | Vol.65, Issue.5 | | Pages 3145-3159

IEEE Transactions on Information Theory

Refined Asymptotics for Rate-Distortion Using Gaussian Codebooks for Arbitrary Sources

Lin ZhouVincent Y. F. TanMehul Motani  
Abstract

The rate-distortion saddle-point problem considered by Lapidoth (1997) consists in finding the minimum rate to compress an arbitrary ergodic source when one is constrained to use a random Gaussian codebook and minimum (Euclidean) distance encoding is employed. We extend Lapidoth's analysis in several directions in this paper. First, we consider refined asymptotics. In particular, when the source is stationary and memoryless, we establish the second-order, moderate, and large deviation asymptotics of the problem. Second, by random Gaussian codebook, Lapidoth referred to a collection of random codewords, each of which is drawn independently and uniformly from the surface of an n -dimensional sphere. To be more precise, we term this as a spherical codebook. We also consider i.i.d. Gaussian codebooks in which each random codeword is drawn independently from a product Gaussian distribution. We derive the second-order, moderate, and large deviation asymptotics when i.i.d. Gaussian codebooks are employed. In contrast to the recent work on the channel coding counterpart by Scarlett, Tan, and Durisi (2017), the dispersions for spherical and i.i.d. Gaussian codebooks are identical. The ensemble excess-distortion exponents for both spherical and i.i.d. Gaussian codebooks are established for all rates. Furthermore, we show that the i.i.d. Gaussian codebook has a strictly larger excess-distortion exponent than its spherical counterpart for any rate greater than the ensemble rate-distortion function derived by Lapidoth.

Original Text (This is the original text for your reference.)

Refined Asymptotics for Rate-Distortion Using Gaussian Codebooks for Arbitrary Sources

The rate-distortion saddle-point problem considered by Lapidoth (1997) consists in finding the minimum rate to compress an arbitrary ergodic source when one is constrained to use a random Gaussian codebook and minimum (Euclidean) distance encoding is employed. We extend Lapidoth's analysis in several directions in this paper. First, we consider refined asymptotics. In particular, when the source is stationary and memoryless, we establish the second-order, moderate, and large deviation asymptotics of the problem. Second, by random Gaussian codebook, Lapidoth referred to a collection of random codewords, each of which is drawn independently and uniformly from the surface of an n -dimensional sphere. To be more precise, we term this as a spherical codebook. We also consider i.i.d. Gaussian codebooks in which each random codeword is drawn independently from a product Gaussian distribution. We derive the second-order, moderate, and large deviation asymptotics when i.i.d. Gaussian codebooks are employed. In contrast to the recent work on the channel coding counterpart by Scarlett, Tan, and Durisi (2017), the dispersions for spherical and i.i.d. Gaussian codebooks are identical. The ensemble excess-distortion exponents for both spherical and i.i.d. Gaussian codebooks are established for all rates. Furthermore, we show that the i.i.d. Gaussian codebook has a strictly larger excess-distortion exponent than its spherical counterpart for any rate greater than the ensemble rate-distortion function derived by Lapidoth.

+More

Cite this article
APA

APA

MLA

Chicago

Lin ZhouVincent Y. F. TanMehul Motani,.Refined Asymptotics for Rate-Distortion Using Gaussian Codebooks for Arbitrary Sources. 65 (5),3145-3159.

Disclaimer: The translated content is provided by third-party translation service providers, and IKCEST shall not assume any responsibility for the accuracy and legality of the content.
Translate engine
Article's language
English
中文
Pусск
Français
Español
العربية
Português
Kikongo
Dutch
kiswahili
هَوُسَ
IsiZulu
Action
Recommended articles

Report

Select your report category*



Reason*



By pressing send, your feedback will be used to improve IKCEST. Your privacy will be protected.

Submit
Cancel