Towards an automated soft proofing system using high dynamic range imaging and artificial neural networks
Bibliographic record
Abstract
In this thesis, an adaptive mechanism for controlling the illumination is combined with a closed loop technique and the use of High Dynamic range (HDR) to generate a black box model that can simulate the hard proof of a given digital image. An adaptive Artificial Neural Network (ANN) was used to create the black box model, using the camera as a measuring device. The non-uniformity of the illumination in the viewing booth is typically a barrier in creating such a black box model since color appearance varies with location in the viewing booth. This issue was addressed in this thesis by compensation for viewing booth illumination using an inexpensive camera and a Liquid Crystal Display (LCD) projector. HDR was found to give a favourable representation that is more indicative of the image perceived by the operator, and was used as the basis for mapping the original image to the soft proof. A proof of concept was also developed to highlight the utility of the LCD projector based approach in providing a more broad range of varying intensity color illuminants (thus environments) under which a proof may be not only viewed, but modeled through the closed loop process. In this sense, a system has been developed to generate and provide custom soft proofs that can extend the functionality of the standard viewing booth. The proposed technique will open the doors to new automated systems that can be very beneficial to the printing industry.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".