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New dataset SR-Ground enhances image quality assessment for super-resolution

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SR-Ground, a new large-scale dataset designed for fine-grained artifact segmentation in super-resolved images. This dataset, featuring pixel-level annotations for multiple artifact categories, aims to improve the interpretability of Image Quality Assessment (IQA) models. By training IQA models with grounding capabilities on SR-Ground, performance on downstream tasks is significantly enhanced, and a fine-tuning pipeline is demonstrated to reduce perceptible artifacts in super-resolved outputs. AI

IMPACT Enhances the interpretability and effectiveness of image quality assessment models for super-resolution tasks.

RANK_REASON The cluster describes a new academic paper introducing a dataset and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset SR-Ground enhances image quality assessment for super-resolution

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The cluster describes a new academic paper introducing a dataset and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Artem Borisov, Evgeney Bogatyrev, Khaled Abud, Dmitriy Vatolin ·

    SR-Ground: Image Quality Grounding for Super-Resolved Content

    arXiv:2605.21244v2 Announce Type: replace Abstract: Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new types of visual artifacts. While existing Image Quality Assessment (IQA) met…

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