Bibliographic record
Abstract
In this paper, we present a subsampling method for color video sequences using feedforward artificial neural networks. In our method, subsampling of the high detail and smooth regions of the video chrominance frames is performed using pattern matching, by extracting different geometrical information for each region. Experimental results show that our method outperforms spatial subsampling obtained via lowpass filtering and decimation both objectively and subjectively. Moreover, our method significantly outperforms other FANN--based subsampling methods, especially at very low bit rates. 1 INTRODUCTION Due to the lower sensitivity of the human visual system to color information, as compared to its sensitivity to the luminance information [1, 2], chrominance frames are usually subsampled in color video coding, while the luminance component is not changed. To avoid aliasing, signal conditioning in the form of lowpass filtering is commonly performed prior to downsampling [3]. When applied...
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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".