Determinants of perceived image quality: ghosting vs. brightness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The physical specifications of stereoscopic eyewear are routinely documented. However, their effects on the appearance or perceived quality of 3D images is most often evaluated superficially, if at all. Here we apply psychophysical techniques to assess the influence of ghosting and perceived brightness on judgements of image quality. To determine which of these variables has the largest impact we simulated several levels of ghosting and brightness in a digital version of a 70mm 3D image sequence. We then presented these image sequences in a large-format 3D theatre and used a magnitude estimation task to assess image quality. The data were clear in showing a significant effect of ghosting on perceived quality but no effect of image brightness. From this we argue that image ghosting is a critical determinant of perceived image quality and should be a primary consideration in relevant technology decisions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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