Maximum Entropy Spectral Modeling Approach to Mesopic Tone Mapping
Bibliographic record
Abstract
Tone mapping algorithms should be informed by accurate color appearance models (CAM) in order that the perceptual fidelity of the rendering is maintained by the tone mapping transformations. Current tone mapping techniques, however, suffer from a lack of good color appearance models for mesopic conditions. There are only a few currently available appearance models suited to the mesopic range, none of which perform very well. In this paper, we evaluate some of the most prominent models available for mesopic and scotopic vision and, in particular, we focus on the iCAM06 model as one of the best-known tone reproduction techniques. We introduce a spectral-based color appearance model for mesopic conditions which can be incorporated in tone reproduction methods. Based on the maximum entropy spectral modeling approach of Clark and Skaff [1], this is a powerful color appearance model which can predict the color appearance under mesopic conditions as well as under photopic conditions. Our model incorporates the CIE system for mesopic photometry, leading to increased accuracy of color appearance model. At low (mesopic) light levels two factors come into play as compared with high light level (photopic) spectral modeling. The first is that image noise becomes significant. The Clark and Skaff model treats the noise as an inherent part of the modeling process, and an estimate of the noise level sets the tradeoff between the consistency of the solution with the measurements and the spectral smoothing imposed by the maximum entropy constraint. The second factor in mesopic vision is that both the rod and the cone systems are active, requiring a modification to the sensor model. The relative contribution of the rod and cone systems is dependent on the overall light level in this regime, and our approach is adaptive in this sense. We present several experiments comparing the performance of our tone mapping approach with that of the existing methods, showing that the proposed method works very well in this regard, and also demonstrates the potential of our model to become a part of wide-range tone mapping systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".