Human Visual System-Based Saliency Detection for High Dynamic Range Content
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The human visual system (HVS) attempts to select salient areas to reduce cognitive processing efforts. Computational models of visual attention try to predict the most relevant and important areas of videos or images viewed by the human eye. Such models, in turn, can be applied to areas such as computer graphics, video coding, and quality assessment. Although several models have been proposed, only one of them is applicable to high dynamic range (HDR) image content, and no work has been done for HDR videos. Moreover, the main shortcoming of the existing models is that they cannot simulate the characteristics of HVS under the wide luminous range found in HDR content. This paper addresses these issues by presenting a computational approach to model the bottom-up visual saliency for HDR input by combining spatial and temporal visual features. An analysis of eye movement data affirms the effectiveness of the proposed model. Comparisons employing three well-known quantitative metrics show that the proposed model substantially improves predictions of visual attention for HDR content.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it