AI & ML
Third-Order Asymptotics for Fixed-Length Lossy Compression of Discrete Memoryless Sources
arXiv math.IT0 views
arXiv:2611.13573v1 Announce Type: new Abstract: This paper determines the third-order asymptotics of the logarithm of the minimum codebook size for fixed-length lossy compression of discrete memoryless sources. For an excess-distortion probability constraint $\varepsilon\in(0,1)$ and every interior distortion level, we establish a third-order correction of $\frac12\log n+O(1)$, where $n$ is the blocklength. This sharpens the general bounds of Kostina and Verd\'u [IEEE Trans. Inf. Theory, 2012]. The main technical innovation lies in the converse for positive dispersion. Under a reverse change of measure, we retain the exponential weights associated with tilted information and distortion slack and control them jointly using the multivariate weighted-strip estimate of Scarlett, Martinez, and Guill\'en i F\`abregas [Proc. IEEE ISIT, 2015]. A pairing argument verifies the required covariance nondegeneracy uniformly over all reconstruction words whose distortion balls meet a typical set, yielding the matching converse.
Source:https://arxiv.org/abs/2611.13573PreviousEuclidean Hulls of Double Cyclic Codes of Arbitrary Block LengthsNextUnified Far-field and Near-field Codebook Design for XL-array Systems
Related posts
I wanted one screen that said whether a kids' app was worth installing
0dev.to

Before I Could Build GPT, I Had to Teach a Machine to Learn
0dev.to
Cogent and OpenAI build cyber defense factories for enterprises
0OpenAI Blog
ChatGPT Plus for veterans and servicemembers
0OpenAI Blog
Hitting 200 Strokes: What the Signature Maker Does at Its Stroke Limit
0dev.to
A Lightweight, Open-Source Network Digital Twin Blueprint for IoT-Edge Environments
0arXiv cs.NI