Optimal local dimming for LC image formation with controllable backlighting
Why this work is in the frame
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Bibliographic record
Abstract
Light emitting diode (LED)-backlit liquid crystal displays (LCDs) hold the promise of improving image quality while reducing the energy consumption with signal-dependent local dimming. However, most existing local dimming algorithms are mostly motivated by simple implementation, and they often lack concern for visual quality. To fully realize the potential of LED-backlit LCDs and reduce the artifacts that often occur in current systems, we propose a novel local dimming technique that can achieve the theoretical highest fidelity of intensity reproduction in either l(1) or l(2) metrics. Both the exact and fast approximate versions of the optimal local dimming algorithm are proposed. Simulation results demonstrate superior performances of the proposed algorithm in terms of visual quality and power consumption.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| 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