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arXiv · 2610.09077

DISRQAD: Diffusion Image Super-Resolution Quality Assessment Dataset and Benchmark

Nikita Kukuzei·Artem Borisov·Evgeney Bogatyrev·Khaled Abud·Egor Chistov·Dmitriy Vatolin
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Abstract

Diffusion-based image super-resolution (SR) can create visually plausible detail that is not supported by the low-resolution input. We introduce DISRQAD, a subjective-quality dataset and diagnostic benchmark for this setting. It contains mean opinion scores (MOS) for 14,000 SR outputs from ten diffusion and four non-diffusion methods, spanning four low-resolution degradation conditions and x2/x4 upscaling. We evaluate 51 standard full-reference and no-reference metric configurations and 11 adapted variants. Agreement with MOS is substantially weaker on diffusion outputs: the strongest standard no-reference baseline reaches 0.431 SRCC on diffusion SR versus 0.813 on non-diffusion SR. As a case study in benchmark use, a pruned and distilled Q-ReAlign-mini student reaches 0.496 SRCC on diffusion SR. DISRQAD measures perceived output quality, not faithfulness to the input; it enables analysis of metric behavior across generator families and input conditions. Our findings reveal a substantial gap in the assessment of diffusion-based SR and provide a basis for developing quality models sensitive to diffusion-specific artifacts.

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Nikita Kukuzei, Artem Borisov, Evgeney Bogatyrev, Khaled Abud, Egor Chistov, Dmitriy Vatolin. 2026-10-06. DISRQAD: Diffusion Image Super-Resolution Quality Assessment Dataset and Benchmark. https://arxiv.org/abs/2610.09077

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