Demystifying High-Dynamic-Range Technology: A new evolution in digital media
Bibliographic record
Abstract
High-dynamic-range (HDR) technology has attracted a lot of attention recently, especially in commercial trade shows such as the Consumer Electronics Show, the National Association of Broadcasters Show, the International Broadcasting Convention, and Internationale Funkausstellung Berlin. However, a great deal of mystery still surrounds this new evolution in digital media. In a nutshell, HDR technology aims at capturing, distributing, and displaying a range of luminance and color values that better correspond to what the human eye can perceive. Here, the term luminance stands for the photometric quantity of light arriving at the human eye measured in candela per square meter or nits. The color refers to all the weighted combinations of spectral wavelengths, expressed in nanometers (nm), emitted by the sun that are visible by the human eye (see Figure 1). The human eye can perceive a dynamic range of over 14 orders of magnitude (i.e., the difference in powers of ten between highest and lowest luminance value) in the real world through adaptation. However, at a single adaptation time, the human eye can only resolve up to five orders of magnitude, as illustrated in Figure 2. Dynamic range denotes the ratio between the highest and lowest luminance value. As reported in Table 1, there are different interpretations for dynamic range, depending on the application. For instance, in photography, dynamic range is measured in terms of f-stops, which correspond to the number of times that the light intensity can be doubled.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".