IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment
This work addresses the problem of accurate evaluation for text-driven image editing, which is crucial for researchers and developers in computer vision and AI, though it is incremental as it builds on existing assessment frameworks.
The paper tackles the challenge of evaluating text-driven image editing by introducing IE-Bench, a benchmark suite with a dataset of 3,010 human ratings and IE-QA, a new assessment method that shows superior alignment with human perception compared to previous metrics.
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.