# Rate-Distortion Theory in Coding for Machines and its Application

arXiv: https://arxiv.org/abs/2305.17295 · HTML: https://mlsanity.com/paper/2305.17295/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2023.

## Built on by (most cited first, 13 of 13)

- [Learned Scalable Video Coding For Humans and Machines](https://mlsanity.com/paper/2307.08978/) (2023)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models](https://mlsanity.com/paper/2401.07957/) (2024)
- [Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification](https://mlsanity.com/paper/2601.20742/) (2026)
- [Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding](https://mlsanity.com/paper/2602.12866/) (2026)
- [Rate-Distortion Guided Knowledge Graph Construction from Lecture Notes Using Gromov-Wasserstein Optimal Transport](https://mlsanity.com/paper/2511.14595/) (2025)
- [Learned Multimodal Compression for Autonomous Driving](https://mlsanity.com/paper/2408.08211/) (2024)
- [Understanding Rate-Distortion Performance in Distributed Transformer Inference](https://mlsanity.com/paper/2601.22002/) (2026)
- [Prompt-Guided Prefiltering for VLM Image Compression](https://mlsanity.com/paper/2604.00314/) (2026)
- [CI-ICM: Channel Importance-driven Learned Image Coding for Machines](https://mlsanity.com/paper/2604.05347/) (2026)
- [ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality](https://mlsanity.com/paper/2605.09479/) (2026)
- [On Task Scope and Information Retention in Source Coding](https://mlsanity.com/paper/2609.37575/) (2026)
- [Making Language the Low-Rate Code of Vision: Successive Refinement from Words to Pixels](https://mlsanity.com/paper/2610.16552/) (2026)

## Abstract (arXiv)

Recent years have seen a tremendous growth in both the capability and popularity of automatic machine analysis of images and video. As a result, a growing need for efficient compression methods optimized for machine vision, rather than human vision, has emerged. To meet this growing demand, several methods have been developed for image and video coding for machines. Unfortunately, while there is a substantial body of knowledge regarding rate-distortion theory for human vision, the same cannot be said of machine analysis. In this paper, we extend the current rate-distortion theory for machines, providing insight into important design considerations of machine-vision codecs. We then utilize this newfound understanding to improve several methods for learnable image coding for machines. Our proposed methods achieve state-of-the-art rate-distortion performance on several computer vision tasks such as classification, instance segmentation, and object detection.