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Bibliographic Details
Main Authors: Iosevich, A., Vagharshakyan, A., Wyman, E.
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.03178
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author Iosevich, A.
Vagharshakyan, A.
Wyman, E.
author_facet Iosevich, A.
Vagharshakyan, A.
Wyman, E.
contents We study worst-case signal compression under an $\ell^2$ energy constraint, with coordinate-dependent quantization precisions. The compression problem is reduced to counting lattice points in a diagonal ellipsoid. Under balanced precision profiles, we obtain explicit, dimension-dependent upper bounds on the logarithmic codebook size. The analysis refines Landau's classical lattice point estimates using uniform Bessel bounds due to Olenko and explicit Abel summation.
format Preprint
id arxiv_https___arxiv_org_abs_2604_03178
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle High-Dimensional Signal Compression: Lattice Point Bounds and Metric Entropy
Iosevich, A.
Vagharshakyan, A.
Wyman, E.
Information Theory
Numerical Analysis
Number Theory
94A29 (Primary) 41A46, 11P21 (Secondary)
We study worst-case signal compression under an $\ell^2$ energy constraint, with coordinate-dependent quantization precisions. The compression problem is reduced to counting lattice points in a diagonal ellipsoid. Under balanced precision profiles, we obtain explicit, dimension-dependent upper bounds on the logarithmic codebook size. The analysis refines Landau's classical lattice point estimates using uniform Bessel bounds due to Olenko and explicit Abel summation.
title High-Dimensional Signal Compression: Lattice Point Bounds and Metric Entropy
topic Information Theory
Numerical Analysis
Number Theory
94A29 (Primary) 41A46, 11P21 (Secondary)
url https://arxiv.org/abs/2604.03178