Low-Band-Shifted Hierarchical Backward Motion Estimation, Compensation for Wavelet-Based Video Coding.
Bibliographic record
Abstract
A new framework for block-based backward motion compensation in wavelet scalable video coding scheme is proposed. Motion estimation and compensation are hierarchically conducted in wavelet domain using coarser level lowpass subband in the current frame and synthesized next finer level lowpass subband in the reference frame, hence, the motion information does not need to be transmitted. To alleviate the aliasing effect caused by decimation in wavelet decomposition, the lowpass subband in reference frame are shifted to obtain four subbands, which are then used in motion estimation and compensation to generate final prediction. A flexible quantization scheme and arithmetic coding is used to individually encode the motion compensated subbands without exploiting cross-band correlation. Compared to the motion estimation and compensation scheme without shifting of the lowpass subband, the proposed technique provides around 2 dB improvement in PSNR for compression of full motion sequence. The multi-level scalability of the scheme makes it useful for low bandwidth networks, such as satellite or cellular networks.
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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 | 0.000 |
| 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) | 0.001 | 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".