Bibliographic record
Abstract
Autofocus (AF) is the process of moving the camera's lens such that desired scene content is in focus. AF for single image capture is a well-studied research topic and most modern cameras have hardware support that allows quick lens movements to optimize image sharpness. How to best perform AF for video is less clear. Conventional wisdom would suggest that each temporal frame should be as sharp as possible. However, unlike single image capture, the effects of the lens movement is visible in the captured video. As a result, there are two parameters to consider in AF for video: sharpness and lens movement. In this paper, we show that users preferred videos with smooth lens movement, even if it results in less overall sharpness. Based on this observation, we propose two novel AF algorithms for video that strive for both smooth lens movement and sharp scene content. Specifically, we introduce (1) a bidirectional long short-term memory (BLSTM) module trained on smooth lens trajectories and (2) a simple weighted moving average (WMA) method that factors in prior lens motion. Both of these methods have demonstrated excellent results in terms of reducing lens movements (up to 64% reduction) without greatly affecting the sharpness (less than 5.2% change in sharpness). Moreover, videos produced using our methods are more preferred by users over conventional AF that aims only for maximizing sharpness.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".