Motion-Compensated Frame Rate Up-Conversion—Part II: New Algorithms for Frame Interpolation
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
Motion-compensated frame rate up-conversion (MC-FRUC) consists of two key elements: motion estimation and motion-compensated frame interpolation. The motion estimation algorithm presented in , which is used in the MC-FRUC method proposed in this paper, provides unidirectional motion trajectories. The advantage of this motion estimation algorithm, besides its accuracy, is that it provides information on occlusions. However, motion compensation along unidirectional motion trajectories yields overlaps, holes, and blocking artifacts. To solve these problems, this paper presents two new algorithms for unidirectional motion-compensated frame interpolation: irregular-grid expanded-block weighted motion compensation (IEWMC) and block-wise directional hole interpolation (BDHI). The IEWMC is used to reduce the blocking artifacts and solve the problem of overlapping blocks. The BDHI preserves local texture and edges while filling holes. Experimental results show that the IEWMC outperforms conventional motion compensation, and the BDHI is better than the repeated median filter that is often used to fill holes. The performance of the proposed MC-FRUC, that uses the two new algorithms and the unidirectional motion estimation algorithm, is evaluated against three existing MC-FRUC techniques: a typical bi-directional algorithm, an object-based algorithm, and a commercial plug-in product. Experimental results show that the quality of the pictures interpolated using the proposed MC-FRUC method is much higher than those interpolated using the three existing MC-FRUC techniques.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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