# IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment

arXiv: https://arxiv.org/abs/2501.09927 · HTML: https://mlsanity.com/paper/2501.09927/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2025.

## Built on by (most cited first, 10 of 10)

- [Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2505.02567/) (2025)
- [VideoGen-Eval: Agent-based System for Video Generation Evaluation](https://mlsanity.com/paper/2503.23452/) (2025)
- [GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing](https://mlsanity.com/paper/2505.11493/) (2025)
- [Flow4Agent: Long-form Video Understanding via Motion Prior from Optical Flow](https://mlsanity.com/paper/2510.05836/) (2025)
- [What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models](https://mlsanity.com/paper/2503.17482/) (2025)
- [ADIEE: Automatic Dataset Creation and Scorer for Instruction-Guided Image Editing Evaluation](https://mlsanity.com/paper/2507.07317/) (2025)
- [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://mlsanity.com/paper/2603.14916/) (2026)
- [ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services](https://mlsanity.com/paper/2604.24023/) (2026)
- [DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation](https://mlsanity.com/paper/2603.08090/) (2026)
- [MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing](https://mlsanity.com/paper/2608.02059/) (2026)

## Abstract (arXiv)

Recent advances in text-driven image editing have been significant, yet the task of accurately evaluating these edited images continues to pose a considerable challenge. Different from the assessment of text-driven image generation, text-driven image editing is characterized by simultaneously conditioning on both text and a source image. The edited images often retain an intrinsic connection to the original image, which dynamically change with the semantics of the text. However, previous methods tend to solely focus on text-image alignment or have not aligned with human perception. In this work, we introduce the Text-driven Image Editing Benchmark suite (IE-Bench) to enhance the assessment of text-driven edited images. IE-Bench includes a database contains diverse source images, various editing prompts and the corresponding results different editing methods, and total 3,010 Mean Opinion Scores (MOS) provided by 25 human subjects. Furthermore, we introduce IE-QA, a multi-modality source-aware quality assessment method for text-driven image editing. To the best of our knowledge, IE-Bench offers the first IQA dataset and model tailored for text-driven image editing. Extensive experiments demonstrate IE-QA's superior subjective-alignments on the text-driven image editing task compared with previous metrics. We will make all related data and code available to the public.