[2608.14494] Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Information Theory arXiv:2608.14494 (cs) [Submitted on 14 Aug 2026] Title: Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds Authors: Galen Reeves , Ramji Venkataramanan View a PDF of the paper titled Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds, by Galen Reeves and Ramji Venkataramanan View PDF HTML (experimental) Abstract: We study sparse regression codes (SPARCs) for lossy compression under simple greedy encoding rules, including both correlation-based and distance-based methods. We generalize the SPARC construction, and consider the class of \emph{additive orthogonal} regression codes, of which standard SPARCs are a special case. For this class of codes, we derive nonasymptotic bounds on the squared-error distortion by tracking the evolution of the encoding residual across stages. Our results highlight the role of power allocation in controlling the distortion, allowing us to optimize the allocation based on the parameters of the code. The optimized allocation improves the finite-length compression performance of SPARCs, and our bounds provide distortion guarantees for lower complexity variants of SPARCs, like signed SPARCs and $K$-sparse SPARCs. Subjects: Information Theory (cs.IT) Cite as: arXiv:2608.14494 [cs.IT] (or arXiv:2608.14494v1 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2608.14494 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Galen Reeves [ view email ] [v1] Fri, 14 Aug 2026 17:08:24 UTC (57 KB) Full-text links: Access Paper: View a PDF of the paper titled Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds, by Galen Reeves and Ramji Venkataramanan View PDF HTML (experimental) TeX Source view license Current browse context: cs.IT < prev | next > new | recent | 2026-08 Change to browse by: cs math math.IT References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from