Quantization scheme for high definition video coding based on node-cell pixel structure
Bibliographic record
Abstract
An intra-only video coding algorithm based on a so called node-cell pixel coding structure has been proposed for high definition video by the authors. This novel coding structure divides pixels within one macro-block into node and cell pixels. The node pixels are first encoded using DCT, quantization, and entropy coding. The cell pixels are then interpolated using the reconstructed node pixels, and the corresponding residuals are encoded also using DCT, quantization, and entropy coding. Following this coding scheme, this paper further investigates the relationship of quantization parameters between node and cell pixels. To establish a model of this relationship, an extensive set of experiments were designed to perform both node and cell pixel encoding with different video sequences under different bit rates. The coding performance in terms of both bit-rate and distortion was measured for different combinations of the two quantization parameters. The optimal combinations, which bring the best coding performance, were identified and used to establish the mode. The experimental results show that the quantization parameter of cell pixels should be larger than the quantization parameter of node pixels to achieve a high coding performance. A new quantization scheme is designed based on this QP model and a gain up to 0.5 dB over the previous work is achieved.
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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 | 0.000 | 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 | 0.000 | 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".