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

arXiv:2605.21244 · cs.CV · Submitted 2026-05-20 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "SR-Ground: Image Quality Grounding for Super-Resolved Content".

Jane: Super-Resolution (SR) models, especially diffusion-based ones, introduce subtle yet perceptually significant visual artifacts that existing Image Quality Assessment (IQA) methods fail to distinguish or localize.

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So, we're diving into the paper "SR-Ground: Image Quality Grounding for Super-Resolved Content," and it sounds like this work tackles a real headache in super-resolution. Basically, they introduce a new dataset to help us figure out exactly what kinds of visual flaws diffusion models create when they upscale images.

Jane: That’s right, Tom; the thesis here is that current Image Quality Assessment methods give us only one overall quality score and can't tell the difference between various artifacts in super-resolved pictures, which is a big problem for understanding the quality issues.

Lu: It’s interesting how they frame it; they are specifically focusing on fine-grained artifact segmentation because existing IQA methods just don't have that level of detail.

Meng: From an engineering standpoint, if we can segment these artifacts at the pixel level, that moves us past just knowing something is 'bad' to knowing precisely *why* it’s bad and where it is located.

Lalam: I think this work has huge implications for how we train future generative models because understanding these specific visual errors will allow us to build better constraints into the training process.

Tom: Exactly, Lalam; they claim they developed SR-Ground, a large-scale dataset with pixel-level annotations for six distinct artifact types in super-resolved images.

Jane: Yes, and the construction involved a complex iterative pipeline that started by selecting source images from various IQA and aesthetics datasets.

Lu: The selection process itself is quite sophisticated; they clustered thousands of source images using a composite distance metric that factored in things like CLIP embeddings, ARNIQA quality embeddings, spatial information, the number of segments produced by SAM, and a blockiness metric.

Meng: That sounds incredibly thorough; they weren't just grabbing random images for this project; they curated them based on multiple quality indicators.

Lalam: It shows how important it is to be systematic when building datasets for AI research, Meng; the curation process itself is a key part of the learning experience.

Tom: After generating sixty-three thousand super-resolved images through downscaling and upscaling with various state-of-the-art SR methods, they moved into the annotation phase for this paper.

Jane: They started by using a grounding model trained on Q-Ground100K to get initial masks for six artifact categories: "Blur," "Overexposure," "Noise," "Low-light," and then the specific ones related to SR itself.

Lu: The iterative annotation process is what really sets this up apart; they didn't just rely on one pass; they used a human-in-the-loop refinement stage based on artifact prominence thresholds.

Paper summary: Meng: So, the human annotators weren't just drawing boxes randomly, but were actually verifying whether a visible distortion of that selected type was present within the mask, refining it if it wasn't prominent enough.

Lalam: That human oversight is crucial; it ensures the pixel-level annotations have real-world accuracy rather than just being statistical noise, which really helps when we talk about downstream tasks.

Tom: And what they achieved with this dataset is that training IQA models with grounding capabilities significantly improves their performance on subsequent tasks and enables a fine-tuning pipeline to reduce perceptible artifacts.

Jane: So, the paper demonstrates that having these grounding capabilities directly translates into better results when it comes to reducing those visible distortions in images.

Lu: The evaluation of the grounding models themselves was quite rigorous; they fine-tuned architectures like SegFormer and Mask2Former, which are strong baselines for segmentation tasks.

Meng: They showed that when these models were trained on SR-Ground, they consistently outperformed those trained only on Q-Ground100K, indicating a better ability to recognize distortions beyond just the real-world distortions already present in Q-Ground.

Lalam: That performance gap suggests that learning from this specific set of SR artifacts gives models much more robustness when dealing with the complexities introduced by diffusion-based super-resolution.

Tom: Moving into their proposed method, they introduce a "grounding-guided SR training pipeline" that uses Mask2Former to handle the interactive and controllable super-resolution process.

Jane: This pipeline involves two main passes: first, generating an intermediate high-resolution image HR(zero), and then applying a frozen grounding model to get per-pixel distortion maps from that HR(zero).

Lu: The way they use the SAM model here is clever; it extracts large segments, defined as areas greater than one percent of the total area, up to thirty masks.

Meng: So, if a segment exceeds that size threshold and matches a distortion class above sixty-six percent, they get marked for removal with a value of-one in the mask generation step.

Lalam: That masking mechanism is smart because it prioritizes large, clearly defined distortions for manipulation, which makes the editing process more targeted and controllable.

Tom: The second pass then uses this mask to produce the final high-resolution image HR(one), showing how they learned to recognize, add, and remove specific distortion types in user-specified regions.

Paper summary: Jane: To control this learning process during training, they employ four objectives: Data fidelity (Ldata), Edit consistency (Ledit), Distortion verification (Ldist), and Diffusion regularization (Ldiff).

Lu: The Ledit loss specifically compares HR(zero) and HR(one) only in the regions that weren't edited, while Ldist checks the change in predicted distortion probabilities against the intended edits specified by M.

Meng: That combination of losses seems to be what allows them to enforce that distortions are added or removed as requested while still maintaining the overall coherence of the image.

Lalam: This level of control over how distortions are introduced is really significant; it moves beyond simple quality scoring into true controllable content generation.

Tom: So, we've covered the core of SR-Ground and their interactive training pipeline, which shows how to fine-tune a model for precise artifact manipulation.

Jane: And now we move toward the final part of this discussion where we look at what the authors concluded about this work and its broader impact.

Lu: The main conclusion is that SR-Ground provides a crucial dataset for pixel-level grounding of SR-specific visual artifacts, showing that models fine-tuned on this dataset outperform those trained only on Q-Ground100K.

Meng: That direct comparison with Q-Ground100K is a strong validation point; it proves that the extra effort in creating SR-Ground yields tangible performance gains.

Lalam: The implication here, I think, is that for the future of generative content, we need datasets that provide this level of fine-grained visual understanding so we can build tools that aren't just good at general quality assessment but are actually useful for editing and controlling the output.

Tom: Exactly; this work is about giving AI models the specific vocabulary to understand and manipulate the subtle errors introduced by modern super-resolution techniques.

Jane: So, to wrap up this discussion on "SR-Ground: Image Quality Grounding for Super-Resolved Content," it’s about moving from broad quality scores to actionable, controllable editing capabilities in super-resolution systems.

Lu: The authors flag that their method is focused specifically on segmenting pixel-level distortions, which means it doesn't necessarily cover every single aspect of image degradation.

Meng: That’s a fair limitation; they are concentrating on segmentation rather than trying to model every possible visual effect that could happen during the upscaling process.

Lalam: But even with that focus, achieving control over specific artifacts like blur or noise in a super-resolved image is a huge step toward creating more trustworthy and versatile generative systems.

Conclusion: Tom: So, we've seen how this paper introduces SR-Ground as a dataset for pixel-level artifact grounding in super-resolved images, and now we're getting to wrap up what that actually means for the field.

Jane: Exactly; we’ve explored how they built this large dataset and the interactive training pipeline used to control image quality during super-resolution. Now we need to talk about the core message of SR-Ground itself.

Lu: The title, "SR-Ground," points directly to their main contribution, which is providing that pixel-level grounding for those subtle visual artifacts that existing IQA methods miss.

Meng: From my side, the authors are essentially showing us how to teach a model to not just judge quality generally but to pinpoint exactly *where* a super-resolution technique has gone wrong.

Lalam: This focus on fine-grained visual understanding is really important because it helps us build more nuanced AI systems that can actually respect the subtle errors in generated content, which I think will improve how we perceive and trust digital media.

Tom: Right, Lalam; it’s about moving beyond just a simple quality score to having the ability to identify and correct specific visual distortions on a pixel level.

Jane: And the authors of this paper are really showing us how this grounding capability directly improves performance when we fine-tune AI models for super-resolution tasks.

Lu: I think what’s exciting is that they proved these models trained on SR-Ground perform better than those trained only on the Q-Ground100K dataset, which really validates the value of this specific type of training data.

Meng: That performance gain suggests a more robust approach for developing generative tools because they’re learning to handle real-world distortion patterns that aren't just the standard ones we see in basic quality datasets.

Lalam: This advance means we can start building AI that doesn't just create pretty pictures, but one where we have more explicit control over how specific visual flaws are introduced during the upscaling process, which could really shape future creative workflows.

Tom: So, to sum up the conclusion of "SR-Ground," it’s that this dataset and its associated training method give us a way to train AI models with a much deeper understanding of super-resolution artifacts than we had before.

Jane: And what's really compelling is how this directly leads to more controllable generation, where we can instruct the AI to add or remove specific types of visual errors in targeted areas.

Lu: It’s a big step because it moves the discussion from just assessing images post-generation to actively controlling the generation process itself, which opens up so many creative avenues for AI development.

Meng: Practically speaking, this means we can build tools that are more predictable and reliable in their output because they've learned to map specific distortion types to specific manipulation commands.

Lalam: And for our culture, I see this as a way to develop AI systems that are not just powerful but also incredibly precise and transparent about how they create visuals, which could fundamentally alter how we interact with synthesized media.

Tom: It sounds like the future of super-resolution isn't just about making images look clearer, but about having the tools to manipulate those visual details intelligently.

Jane: And that's exactly what SR-Ground is equipping us with, giving AI models a new vocabulary for understanding and controlling visual quality in high-resolution content.

Lomonosov Moscow State University

cs.CV

Submitted: 2026-05-20

Updated: 2026-09-30

Importance score: 66/100

The gist: Super-Resolution (SR) models, especially diffusion-based ones, introduce subtle yet perceptually significant visual artifacts that existing Image Quality Assessment (IQA) methods fail to distinguish

Key concepts

SR-Ground Dataset Construction
This involved generating 63,000 super-resolved images by upscaling various source images using different state-of-the-art methods. Annotations were created through an iterative process where human annotators refined initial segmentation masks to accurately label six specific artifact types in the super-resolved output.
Grounding Model Performance
Models like Mask2Former were evaluated by fine-tuning them on SR-Ground versus a smaller dataset. The results showed that models trained on the larger SR-Ground dataset consistently achieved better performance metrics (F1 score and IoU) when identifying these specific visual distortions compared to models trained only on Q-Ground100K.
Interactive Super-Resolution Pipeline
This is a training method that uses grounding predictions to control super-resolution. It involves generating an initial HR image, applying a grounding model to find per-pixel distortion maps, and then using these maps to selectively add or remove specific artifacts during the second pass of upscaling.
Grounding-Guided Fine-Tuning
This training strategy uses four loss functions—data fidelity, edit consistency, distortion verification, and diffusion regularization—to teach the model to recognize and manipulate specific distortions. This allows the model to learn how to add or remove targeted artifacts in user-specified areas while maintaining overall image quality.

Terminology

Summary

Super-Resolution (SR) models, especially diffusion-based ones, introduce subtle yet perceptually significant visual artifacts that existing Image Quality Assessment (IQA) methods fail to distinguish or localize. This paper introduces SR-Ground, a large-scale dataset specifically designed for fine-grained artifact segmentation in super-resolved images, demonstrating that training IQA models with grounding capabilities significantly improves performance on downstream tasks and enabling a fine-tuning pipeline to reduce perceptible artifacts.

SR-Ground Dataset Construction

The SR-Ground dataset comprises 63,000 images spanning 6 distinct artifact types, providing pixel-level annotations for fine-grained analysis. The construction involved an iterative data generation and refinement pipeline, which included:

  1. Source Image Selection: Images were drawn from large-scale IQA and aesthetics datasets (like AVA, Waterloo Exploration, FLIVE, KonIQ-10K) and clustered into 1,000 groups using a composite distance metric based on semantic embeddings (CLIP), perceptual quality embeddings (ARNIQA), spatial information (SI), the number of segments produced by SAM, and a blockiness metric.

  2. SR Data Generation: Each source image was downsampled using bicubic interpolation with scaling factors of 2× and 4×, optionally subjected to Gaussian blurring, and then upscaled using a diverse set of state-of-the-art SR methods (e.g., RealSR, BSRGAN, SwinIR). This process yielded a total of 63,000 super-resolved images.

  3. Initial Annotation: A grounding model was trained on Q-Ground100K to produce initial artifact segmentation masks for the six categories: Blur, Overexposure, Noise, Low-light, SR-specific artifacts.

  4. Human-in-the-Loop Refinement: Annotators were presented with a pair of images (SR and LR), indicating whether a visible distortion of the selected type was present within the highlighted mask. Masks with prominence below 50% were considered absent, and this process was repeated iteratively to refine the annotations, resulting in the final 63,000 annotated images.

Grounding Model Performance

The paper evaluates grounding models by fine-tuning state-of-the-art architectures like SegFormer and Mask2Former. When training on Q-Ground100K, only five classes are supervised; however, full six-class supervision is applied when training on SR-Ground. The results show that models fine-tuned on SR-Ground consistently outperform those trained only on Q-Ground100K, indicating improved robustness beyond real-world distortions. For example, Mask2Former fine-tuned on SR-Ground achieved an F1 score of 0.1618 and an IoU of 0.3724 on the DeSRA dataset when compared to models trained only on Q-Ground and Open Images [23].

Interactive Super-Resolution Pipeline

The authors propose a grounding-guided SR training pipeline that leverages grounding predictions to mitigate artifact formation during training, using a Mask2Former-based framework. This pipeline extends the OSEDiff model to enable interactive and controllable super-resolution. The process involves two passes:

  1. First pass: The model generates HR(0) = Gtheta (xLQ, 0).

  2. Grounding application: A frozen grounding model is applied to HR(0) to obtain per-pixel distortion maps. SAM is then used to extract large segments (> 1% area, up to 30 masks).

  3. Mask generation: Segments are matched to distortion classes via overlap; if a class exceeds 66%, it is assigned a value of-1 (removal); otherwise, a random class is assigned +1 (addition).

  4. Second pass: The resulting mask M is used to produce HR(1) = Gtheta (HR(0), M).

Grounding-Guided Fine-Tuning for Control

Training for the interactive pipeline utilizes four objectives: Data fidelity (Ldata), Edit consistency (Ledit), Distortion verification (Ldist), and Diffusion regularization (Ldiff). The Ledit loss compares HR(0) and HR(1) to xGT only in non-edited regions, while Ldist compares the change in predicted distortion probabilities between HR(0) and HR(1) against the intended edits specified by M. This mechanism ensures that distortions are added or removed as requested while preserving global image coherence. The resulting model successfully learns to recognize, add, and remove specific distortion types in user-specified regions.

Conclusion

The work introduces SR-Ground, a dataset for pixel-level grounding of SR-specific visual artifacts, and demonstrates that models fine-tuned on this dataset outperform those trained only on Q-Ground100K.

Improvements for AI systems

Here are specific improvements for AI systems based on the SR-Ground paper, categorized by application:

) Improvement 1: Development of Explainable and Fine-Grained Image Quality Assessment (IQA) Models.

The improved AI system will transition from providing a single, holistic quality score to offering a detailed, spatially localized diagnostic report.

Specific capabilities of the improved system:

  1. Automatic segmentation of super-resolved images into six distinct artifact categories (Blur, Overexposure, Noise, Low-light, SR-specific artifacts).

  2. Providing pixel-level maps indicating the exact spatial location and severity of each artifact type within an image.

  3. Enabling researchers to quantify the contribution of specific SR model types (CNN vs. Diffusion) to observed visual degradation by analyzing the distribution of SR-specific artifacts across different models in a dataset like SR-Ground.

Application: Used in automated content moderation for generative AI outputs, quality control for high-fidelity media production, and debugging generative models to pinpoint why specific artifacts (e.g., texture distortion vs. edge blur) are appearing.

) Improvement 2: Creation of Robust and Interpretable Super-Resolution (SR) Training Pipelines.

The improved AI system will incorporate a grounding-guided training mechanism that actively learns to mitigate known SR artifacts during the generation process, rather than just passively learning from ground truth pixels.

Specific capabilities of the improved system:

  1. Integration of a grounding model (like MaskFormer-SR) into the SR synthesis loop (e.g., within an OSEDiff framework).

  2. During inference or training, the system can be instructed to remove or add specific distortions in targeted regions based on the learned artifact masks.

  3. Implementation of a distortion verification loss that ensures the generated image not only looks high-quality but also possesses the correct spatial distribution of artifacts according to expert knowledge (i.e., it prevents introducing unwanted SR artifacts).

Application: Developing next-generation, controllable SR models where users can specify desired fidelity while maintaining specific quality characteristics (e.g., Upscale this image, but ensure no synthetic noise is added to the sky region). This moves SR from a black-box restoration task to a controllable editing task.

) Improvement 3: Automated and Scalable Dataset Curation for Fine-Grained Visual Tasks.

The improved AI system will include an automated iterative data curation pipeline that efficiently generates high-quality, labeled datasets for novel visual tasks without relying solely on expensive, manual annotation of every image.

Specific capabilities of the improved system:

  1. A multi-stage pipeline combining semantic feature clustering (using CLIP/ARNIQA) and structural metrics (SAM segments, Blockiness Estimator) to select a maximally diverse set of source images.

  2. A Human-in-the-Loop refinement stage utilizing prominence scoring to filter noisy annotations generated by AI models (like VLM annotators), ensuring high label consistency across the entire dataset.

  3. A mechanism for iterative self-improvement where the grounding model is continuously fine-tuned on newly labeled data, leading to a co-evolution of the dataset and the model.

Application: Accelerating R&D in any domain requiring fine-grained visual understanding (e.g., medical image analysis, satellite imagery classification). This system drastically reduces the cost and time associated with creating specialized datasets for complex visual problems.

Sources

Related papers