Panoptic Pairwise Distortion Graph
In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods focus on whole image analysis, while implicitly relying on region-level understanding. We extend the intra-image notion of a scene graph to inter-image, and propose a novel task of Distortion Graph (DG). DG treats paired images as a structured topology grounded in regions, and represents dense degradation information such as distortion type, severity, comparison and quality score in a compact interpretable graph structure. To realize the task of learning a distortion graph, we contribute (i) a region-level dataset, PandaSet, (ii) a benchmark suite, PandaBench, with varying region-level difficulty, and (iii) an efficient architecture, Panda, to generate distortion graphs. We demonstrate that PandaBench poses a significant challenge for state-of-the-art multimodal large language models (MLLMs) as they fail to understand region-level degradations even when fed with explicit region cues. We show that training on PandaSet or prompting with DG elicits region-wise distortion understanding, opening a new direction for fine-grained, structured pairwise image assessment.
69de5a42b2ac4331b6f6d907
Muhammad Kamran Janjua
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2026-04-15T12:06:25.032Z
67181e3febabfc1eb05136f2
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/OwaeHzhmYq8_XgXDNd6z5.png
Kamran Janjua
kjanjua26
user
kjanjua26
69de5a42b2ac4331b6f6d908
Abdul Wahab
69de5a42b2ac4331b6f6d909
Bahador Rashidi
Panoptic Pairwise Distortion Graph
In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods focus on whole image analysis, while implicitly relying on region-level understanding. We extend the intra-image notion of a scene graph to inter-image, and propose a novel task of Distortion Graph (DG). DG treats paired images as a structured topology grounded in regions, and represents dense degradation information such as distortion type, severity, comparison and quality score in a compact interpretable graph structure. To realize the task of learning a distortion graph, we contribute (i) a region-level dataset, PandaSet, (ii) a benchmark suite, PandaBench, with varying region-level difficulty, and (iii) an efficient architecture, Panda, to generate distortion graphs. We demonstrate that PandaBench poses a significant challenge for state-of-the-art multimodal large language models (MLLMs) as they fail to understand region-level degradations even when fed with explicit region cues. We show that training on PandaSet or prompting with DG elicits region-wise distortion understanding, opening a new direction for fine-grained, structured pairwise image assessment.
Researchers introduce a novel approach to image assessment by representing image pairs as structured distortion graphs that capture region-level degradation information, challenging existing multimodal models' ability to understand fine-grained visual differences.
scene graph distortion graph region-level dataset PandaSet benchmark suite PandaBench efficient architecture Panda multimodal large language models MLLMs