Bibliographic record
Abstract
Most video rate-control research emphasizes constant bit-rate (CBR) applications. These aim to produce a CBR bitstream with the highest possible quality, within the bitrate constraint and with no consideration for quality variation. In this paper, two MPEG-4 Constant-Quality (CQ) CBR controls are proposed. These aim to produce a CBR bitstream that meets a target quality level whenever possible. The Frame-level Laplacian CQ (FLCQ) algorithm uses a distortion model based on a Laplacian model for DCT coefficients. In contrast, the MB-level Viterbi CQ (MVCQ) algorithm uses the Viterbi algorithm to determine the best combination of MB-QP’s. “CQ” is measured by the deviation of the mean quality from the target quality, and by quality variance over time. Simulation results suggest that the proposed algorithms perform better than Q2 and TM5 under these measures. In some cases, they produce bitstreams with fewer bits while having higher average PSNR, and smaller variance. The FLCQ algorithm has more variation in quality than the MVCQ algorithm. With extra computational complexity, the MVCQ algorithm gives the best performance over all algorithms tested. Often, it precisely meets the target PSNR with no variation. This is truly a CQ rate-control algorithm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".