Variable-Sized, Circular Bokeh Depth of Field Effects
Bibliographic record
Abstract
We propose the Flexible Linear-time Area Gather (FLAG) blur algorithm with a variable-sized, circular bokeh for producing depth of field effects on rasterized images. The algorithm is separable (and thus linear). Bokehs can be of any convex shape including circles. The goal is to create a high quality bokeh blur effect by post processing images rendered in real-time by a 3D graphics system. Given only depth and colour information as input, the method performs three passes. The circle of confusion pass calculates the radius of the blur at each pixel and packs the input buffer for the next pass. The horizontal pass samples pixels across each row and outputs a 3D texture packed with blur information. The vertical pass performs a vertical gather on this 3D texture to produce the final blurred image. The time complexity of the algorithm is linear with respect to the maximum radius of the circle of confusion, which compares favorably with the naive algorithm, which is quadratic. The space complexity is linear with respect to the maximum radius of the circle of confusion. The results of our experiments show that the algorithm generates high quality blurred images with variable-sized circular bokehs. The implemented version of the proposed algorithm is consistently faster in practice than the implemented naive algorithm. Although some previous algorithms have provided linear performance scaling and variable sized bokehs, the proposed algorithm provides these while also permitting more flexibility in the allowed blur shapes, including any convex shape.
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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 | 0.000 | 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 | 0.001 | 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 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".