Quantitative Evaluation and Attribute of Overall Brightness in a High Dynamic Range World
Bibliographic record
Abstract
Brightness is an attribute of visual perception to describe how intense the light entering the eye is. Since human perception is not linearly related to light intensity, characterizing brightness is a challenging task. In Standard Dynamic Range (SDR) imagery, brightness is often quantified using the Average Picture Level (APL) which is the average of all pixels' code values normalized by the maximum signal code value. APL provides a simple and commonly used brightness metric for SDR however its validity for High Dynamic Range (HDR) content has never been assessed. Due to the higher luminance range that HDR supports, HDR content are encoded using a different transfer function than SDR. Thus different distribution of pixel's code values is to be expected between HDR and SDR content. In this work, we evaluate the efficiency of the APL metric to quantify brightness of HDR content. We describe, using patches and professionally graded images, pixel's distribution where the APL fails to distinguish relative brightness between pair of images. To overcome APL shortcomings, we propose a brightness metric based on the geometric mean and variance of an image luma code values. We then conduct two subjective experiments to compare the efficiency of APL and our metric. Results show that the proposed metric predicts more accurately the relative brightness between two frames.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".