A content-based rapid video playback method using motion-based video time density function and temporal quantization
Bibliographic record
Abstract
In this paper, we propose a new content-based rapid video playback method using motion-based video time density function (MVTDF) and temporal quantization. In particular, we formulate the rapid video playback problem as a generic sampling problem. We present a novel MVTDF using the inter-frame mutual information in pixel level to describe the time density of video motion activities. A MVTDF-based temporal quantization method is then employed to find the best quanta and partition in time domain. The video frames that are the nearest neighbors to the quanta in the quantization codebook are sampled to navigate the video in a non-uniform way. By selecting the most salient set of frames, the technique is integrated into a video player for variable-rate rapid video playback that preserves content. The implementation of video player demonstrates the feasibility of proposed method in practice. Experimental results show that the proposed method is effective to capture the important semantic information of video data during rapid playback.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".