A novel bit rate reduction method of H.264/AVC intra coding
Bibliographic record
Abstract
H.264/AVC intra encoder uses nine prediction modes in 4×4 and 8×8 block unit to reduce the spatial redundancies. Too many intra modes not only increase the encoder complexity but also increase the number of overhead bits. In order to reduce the number of overhead bits and computational cost, an intra mode bit rate reduction (BRR) method is proposed in this paper. In the proposed method, the numbers of prediction modes for each 4×4 and 8×8 block are selected adaptively. Based on the similarities of the reference pixels, each block is classified as one of three categories. This paper also estimates the most probable mode (MPM) from the prediction mode direction of neighboring blocks which have different weights according to their positions. Experimental results confirm that the proposed method saves 12.4% bit rate, improves the video quality by 0.37 dB on average, and requires 37.5% less computations than H.264/AVC intra encoder.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".