Mantissa-Exponent-Based Tone Mapping for Wide Dynamic Range Image Sensors
Bibliographic record
Abstract
The dynamic range of a scene is defined as the ratio between the maximum and minimum luminance in it. Wide dynamic range (WDR) means this ratio is so large that it exceeds the dynamic range of a traditional image sensor. Nowadays, WDR image sensors enable the capture of WDR scenes. However, the captured WDR image requires an additional tone mapping step to compress the high bit pixel of WDR image to low rate pixel so that it can be displayed on the screen. The tone mapping algorithm is mostly done in an image signal processor or with a specific software application. This brief proposes a tone mapping technique that is suitable for direct processing of the output of a WDR image sensor bitstream. The algorithm acquires statistics on the mantissa and exponent parts of the pixel value and then generates a refined histogram for tone mapping. Experiments that evaluate the image quality and hardware efficiency are carried out. The results indicate that the proposed mantissa exponent-based algorithm provides visually pleasing results and preserves details of the original WDR image better than other similar algorithms. The hardware resources’ efficiency of the algorithm makes the system on chip implementation possible.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".