Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment

• URL: http://arxiv.org/abs/2604.12175v1
• Date: Tue, 14 Apr 2026 01:05:41 GMT
• Title: Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment
• Abstract summary: IEQA requires complex reasoning over multimodal inputs and multi-dimensional assessments.<n>We propose Define-and-Score Image Editing Quality Assessment (DS-IEQA), a unified framework that jointly learns evaluation criteria and score representations.<n>Our method ranks 4th in the 2026 NTIRE X-AIGC Quality Assessment Track 2 without any additional training data.
• Score: 28.60763581601123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA requires complex reasoning over multimodal inputs and multi-dimensional assessments. Existing MLLM-based approaches often rely on human heuristic prompting, leading to two key limitations: rigid metric prompting and distance-agnostic score modeling. These issues hinder alignment with implicit human criteria and fail to capture the continuous structure of score spaces. To address this, we propose Define-and-Score Image Editing Quality Assessment (DS-IEQA), a unified framework that jointly learns evaluation criteria and score representations. Specifically, we introduce Feedback-Driven Metric Prompt Optimization (FDMPO) to automatically refine metric definitions via probabilistic feedback. Furthermore, we propose Token-Decoupled Distance Regression Loss (TDRL), which decouples numerical tokens from language modeling to explicitly model score continuity through expected distance minimization. Extensive experiments show our method's superior performance; it ranks 4th in the 2026 NTIRE X-AIGC Quality Assessment Track 2 without any additional training data.

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