A motion adaptive deinterlacing method with hierarchical motion detection algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a motion adaptive deinterlacing method for high quality conversion of interlace video format to progressive video format. A high performance and low complexity algorithm for motion detection is proposed. This algorithm uses five consecutive interlace video fields for motion detection, so it is able to capture a wide range of motions from slow moving objects to fast motions. The proposed motion detection algorithm benefits from a hierarchal structure where the algorithm starts with detecting motion in large partitions of a given field. Depending on the detected motion activity level, the motion detection algorithm might be recursively applied to sub-blocks of the original partition. Two low pass filters are used during the motion detection to increase the algorithm accuracy. The result of motion detection is then used in a motion adaptive interpolator for high quality deinterlacing. Excellent experimental results are obtained for motion detection and deinterlacing performance.
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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 | 0.000 | 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