Color and luminance correction and calibration system for LED video screens
Bibliographic record
Abstract
Recent years have seen a surge in the popularity of Light emitting diode (LED) video screens, which have come to be a critical part of how the world of show business and corporate events are seen by their audiences. LED video screens are bright, visually attractive, can stand severe weather conditions, and consume far less power than CRT technology. In LED screens technology, pixels are composed of three primary LED colors: red, green, and blue (RGB). Using the primary colored LEDs provide the ability to generate variety of color hues, saturations and values. However, the RGB LEDs in the screen's pixels have different luminance and color due to the LEDs themselves. These differences seriously destroy the white balance of the LED pixels and modules, and make the picture color aberration, blotchy and patchy. To overcome these problems, different techniques and methodologies has been proposed in the literature. The main drawbacks of these techniques are the cost-effectiveness in the sense they provide mediocre resolution. In this thesis, a new and cost-effective methodology and technique is proposed to correct the color and the luminance of LED video screens while maintaining a high quality and high resolution image display. Also, a new developed algorithm is proposed to fit different color and brightness calibration purposes. The proposed algorithm is based on the CIE Commission Internationale de l'Eclairage standards. The technique and methodology have been implemented, in collaboration with LSI SACO Technologies Inc., using fully automated robotic spectrometer system and achieved the targeted goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".