Chromatic blur perception in simple and complex stimul
Bibliographic record
Abstract
In a compelling demonstration Wandell (1995) showed that blurring the chromatic but not luminance layer of an image of a natural scene failed to elicit any impression of blur. Subsequent studies have suggested that the effect is due either to masking of the chromatic blur by the sharp luminance edges in the image (Sharman et al., 2013) or to a relatively compressed transducer function for chromatic blur (Kingdom et al., 2015). To test between these alternatives we first measured points of subjective equality (PSE) and precisions (thresholds) for Gaussian blurred circles. Perceived chromatic blur was found to be equal to perceived luminance blur, and was independent of contrast level. Introducing a sharp luminance step-edge had no effect on the perceived level of chromatic blur. However, in a subsequent experiment using images of natural scenes, the perceived blur of the chromatic layer was reduced in the presence of the luminance layer, as was also the chromatic blur precision (thresholds), in keeping with the results of Sharman et al. (2013). Yet, when the luminance layer was rotated relative to the chromatic layer, which removed the color-luminance edge correlations, chromatic blur precision was even worse, even although PSEs were restored to near-veridicality. We conclude that both luminance masking and chromatic scale compression contribute to the Wandell effect. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".