Evaluation of high dynamic range content viewing experience using eye-tracking data
Bibliographic record
Abstract
High Dynamic Range (HDR) technologies have demonstrated that they can play an influential role in the design of camera and consumer display products. Understanding the human visual experience of viewing HDR content is a crucial aspect of such systems. Although the visual experience of Low Dynamic Range (LDR) technologies have been well explored, there are limited comparable studies for HDR content. In this paper, we present a study that evaluates the viewing experience of HDR and LDR content as measured both subjectively, and objectively vis-a-vis eye-tracking data. The eye-tracking data was collected while individuals viewed HDR or LDR videos in a free-viewing task. Our study shows a clear subjective preference for HDR content when individuals are given a choice between HDR and LDR displays, but this preference does not translate into a reliable difference in the subjective or objective eye movement measures when the displays are viewed sequentially, suggesting that objective performance measures are not the foundation upon which subjective preferences are based. Our findings should help in developing new visual attention models for the role of HDR and LDR content on subjective and objective experience and performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".