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[2602.12866] Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding
Summary
This paper explores Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding, focusing on optimizing bitrate, latency, and task performance in visual data communication systems.
Why It Matters
Understanding the limitations and potential of Task-Oriented Source Coding (TOSC) is crucial for improving machine-centric inference systems. This research provides insights into optimizing performance under resource constraints, which is increasingly relevant in AI and machine learning applications.
Key Takeaways
- Current TOSC schemes operate below theoretical limits.
- Task model-aware rate-distortion bounds address suboptimality and constraints.
- Transmitter-side complexity is a significant bottleneck in performance.
Computer Science > Information Theory arXiv:2602.12866 (cs) [Submitted on 13 Feb 2026] Title:Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding Authors:Andriy Enttsel, Vincent Corlay View a PDF of the paper titled Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding, by Andriy Enttsel and 1 other authors View PDF HTML (experimental) Abstract:Task-Oriented Source Coding (TOSC) has emerged as a paradigm for efficient visual data communication in machine-centric inference systems, where bitrate, latency, and task performance must be jointly optimized under resource constraints. While recent works have proposed rate-distortion bounds for coding for machines, these results often rely on strong assumptions on task identifiability and neglect the impact of deployed task models. In this work, we revisit the fundamental limits of single-TOSC through the lens of indirect rate-distortion theory. We highlight the conditions under which existing rate-distortion bounds are achievable and show their limitations in realistic settings. We then introduce task model-aware rate-distortion bounds that account for task model suboptimality and architectural constraints. Experiments on standard classification benchmarks confirm that current learned TOSC schemes operate far from these limits, highlighting transmitter-side complexity as a key bottleneck. Comments: Subjects: Information Theory (cs.IT); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Sign...