Three-dimensional linear trajectory filtering using the DWT and the mixed-domain approach
Bibliographic record
Abstract
A method is proposed which combines the two-dimensional (2-D) orthogonal discrete-wavelet transform (DWT) and mixed-domain (Mixed-D) filtering to selectively enhance or attenuate a 2-D signal moving along a linear trajectory at a constant velocity. Such signals are common in video coding applications. The 2-D DWT is applied separately to each frame of the three-dimensional (3-D) input sequence, resulting in a set of multiresolution (subband) image sequences. Mixed-D filtering is then used to enhance or reject linear trajectory signals within each subband image sequence. The output for each frame is formed by inverting the wavelet transform using the Mixed-D processed sequences. This method or filtering lends itself to use in subband video coding systems, which are becoming increasingly popular, and permits selective Mixed-D filtering of the subband signals depending upon the energy content within the subbands. In this contribution, we present an overview of the proposed algorithm and present preliminary results which compare favourably in terms of arithmetic complexity, storage requirements, and the subjective quality of output, to those obtained using the Mixed-D technique.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 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".