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.

翻译：面向任务信源编码已成为以机器为中心的推理系统中高效视觉数据通信的一种范式，其必须在资源约束下联合优化比特率、延迟和任务性能。尽管近期研究提出了面向机器编码的率失真界，但这些结果通常依赖于对任务可识别性的强假设，并忽略了已部署任务模型的影响。在本工作中，我们通过间接率失真理论的视角重新审视了单任务面向任务信源编码的基本极限。我们阐明了现有率失真界可达到的条件，并揭示了其在现实场景中的局限性。随后，我们提出了考虑任务模型次优性与架构约束的任务模型感知率失真界。在标准分类基准上的实验证实，当前基于学习的面向任务信源编码方案远未达到这些极限，突显出发送端复杂度是关键的瓶颈所在。