Structure and aesthetics in non-photorealistic images
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
Non-photorealistic rendering (NPR) has been used to produce stylized images, e.g., in a stippled or painted style. To evaluate NPR algorithms, similarity measurements used in image processing have been employed to assess the quality of rendered images. However, there is no standard objective measurement of stylization quality. In many cases, raw side-by-side comparisons are used to demonstrate improvements in aesthetic quality. This means of comparison often fails to be persuasive due to the small size of demonstrations and the subjective choice of images. We conducted a user study and examined responses of 30 subjects in order to determine two things: whether there exists a relationship between the structural quality and aesthetic quality of non-colored non-photorealistic images; and whether the choice of images matters for side-by-side comparisons. Our study revealed a statistically significant correlation between the aesthetic and structure ratings given by participants: increases in structural rating coincided with increases in aesthetic rating. Second, participants' ratings of structure and aesthetic were influenced by image content: that is, choice of input images influenced the results of side-by-side comparisons.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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