Rate-Distortion Optimization

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7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Streaming of rendered content with adaptive frame rate and resolution
May 9, 2026Yaru Liu, Joseph G. March, Rafal K. MantiukRate-Distortion Optimization
University of Cambridge, UK
RDKV: Rate-Distortion Bit Allocation for Joint Eviction and Quantization of the KV Cache
May 8, 2026Junkai Zhang, Hang Guo, Luca Benini +1KV Caching Long-Context Language Model Inference
ETH Zurich · Tsinghua University
LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation
May 7, 2026Dan Jacobellis, Neeraja J. YadwadkarLearned Image Compression Rate-Distortion Optimization
University of Texas at Austin
When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
May 6, 2026Yalin E. Sagduyu, Tugba ErpekSemantic Communication Age of Information
Nexcepta, Gaithersburg, MD, USA
What Matters in Practical Learned Image Compression
May 6, 2026Kedar Tatwawadi, Parisa Rahimzadeh, Zhanghao Sun +5Learned Image Compression Rate-Distortion Optimization
Apple
TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement
Apr 30, 2026Xiumei Li, Alexander Kopte, André KaupPoint Cloud Compression Rate-Distortion Optimization
Chair of Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen, Germany
Statistical Inference and Quality Measures of KV Cache Quantisations Inspired by TurboQuant
Apr 27, 2026Paolo D'AlbertoKV Caching Rate-Distortion Optimization
AMD
Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism
Apr 22, 2026Gustaf Åhlgren, Onur GünlüRate-Distortion Theory Rate-Distortion Optimization
Information Theory and Security Laboratory (ITSL), Linköping University, Sweden · Lehrstuhl für Nachrichtentechnik, Technische Universität Dortmund, Germany
RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory
Apr 22, 2026Fei Zuo, Zikang Zhou, Hao Cong +2LLM Inference Efficiency Mixed-Precision Quantization
1BA TechWorks (BMW Group) · 2National University of Singapore · 3Tsinghua University
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
Apr 16, 2026Zijian Liang, Kai Niu, Changshuo Wang +2Rate-Distortion Theory Rate-Distortion Optimization
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China · State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Same Compression Principle, Different Geometry: Rate-Distortion Signatures Dissociate Biological and Artificial Visual Systems
Mar 2, 2026Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan LinRate-Distortion Theory Rate-Distortion Optimization
Windreich Department of AI and Human Health Icahn School of Medicine at Mount Sinai New York, NY, USA · Department of Computing Imperial College London London, UK · Windreich Department of AI and Human Health Department of Psychiatry Icahn School of Medicine at Mount Sinai New York, NY, USA
On the Optimality of Kinship Naming: an Information-theoretic Approach
Nov 24, 2025Phong Le, Mees Lindeman, Raquel G. AlhamaRate-Distortion Optimization
University of St Andrews · Institute for Logic, Language and Computation; University of Amsterdam.
Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding
Sep 10, 2025Tam Thuc Do, Philip A. Chou, Gene CheungPoint Cloud Compression Rate-Distortion Optimization
department of EECS, York University, 4700 Keele Street, Toronto, M3J 1P3, Canada
LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression
Mar 25, 2023Xi Zhang, Xiaolin WuLearned Image Compression Vector Quantization
Shanghai Jiao Tong University · McMaster University

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Streaming of rendered content with adaptive frame rate and resolution

Yaru Liu, Joseph G. March, Rafal K. Mantiuk

University of Cambridge, UK

Rate-Distortion Optimization

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Streaming rendered content is an attractive way to bring high-quality graphics to billions of mobile devices that do not have sufficient rendering power. Existing solutions render content on a server at a fixed frame rate, typically 30 or 60 frames per second, and reduce resolution when bandwidth is restricted. However, this strategy leads to suboptimal rendering quality under the bandwidth constraints. In this work, we exploit the spatio-temporal limits of the human visual system to improve perceived quality while reducing rendering costs by adaptively adjusting both frame rate and resolution based on scene content and motion. Our approach is codec-agnostic and requires only minimal modifications to existing rendering infrastructure. We propose a system in which a lightweight neural network predicts the optimal combination of frame rate and resolution for a given transmission bandwidth, content, and motion velocity. This prediction significantly enhances perceptual quality while minimizing computational cost under bandwidth constraints. The network is trained on a large dataset of rendered content labeled with a perceptual video quality metric. The dataset and further information can be found at the project web page: https://www.cl.cam.ac.uk/research/rainbow/projects/adaptive_streaming/.

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