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.>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".