Vision transformers for image quality assessment on high dynamic range displays
Bibliographic record
Abstract
Image quality assessment (IQA) plays a critical role in evaluating and optimizing visual experiences by approximating human perception of image quality.However, existing IQA methods often fail to generalize across variations in illumination, display properties, and content, which are all too common in real-world scenarios involving modern display technologies.In this thesis, we address these challenges through a series of contributions spanning perceptual studies and applications of deep learning for IQA.First, we conduct a subjective experiment to quantify the influence of ambient illumination on human perception of image quality.To complement these findings, we introduce a framework that extends the applicability of existing IQA methods to a wider range of illumination and display parameters, effectively modeling viewing conditions from complete darkness to bright daylight.Next, we explore the use of vision transformers (ViTs) for IQA, examining the feature representations of various pre-trained ViTs to gain insights into how these models encode image quality distortions and to identify architectures and training objectives better suited for IQA.Building on this analysis, we introduce Vision Transformer for Attention-Modulated Image Quality (VTAMIQ), a novel full-reference IQA model that leverages ViTs to capture global dependencies in images and achieves state-of-the-art performance on standard IQA datasets.Finally, while most existing IQA methods and datasets are designed for Standard Dynamic Range (SDR) imaging, we address the challenges of training deep IQA models on High Dynamic Range (HDR) data by integrating specialized fine-tuning and domain adaptation techniques.Models trained with our approach outperform previous baselines, converge significantly faster, and generalize more reliably to HDR inputs.Altogether, our findings provide valuable insights into the influence of viewing conditions on human perception of image quality and support the development of more robust and generalizable IQA models, enhancing their adaptability and performance in real-world applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".