Weighting Quantization Matrices for HEVC/H.265-Coded RGB Videos
Bibliographic record
Abstract
In the HEVC/H.265 video coding standard, weighting quantization matrices (WQMs) are supported to take advantage of the characteristics of the human visual system (HVS). However, the default WQMs utilized in HEVC are developed for YCbCr videos instead of RGB videos. In this paper, a set of new WQMs is proposed for video coding in RGB color space. First, we utilize the spatial contrast sensitivity function (CSF) to model the bandpass property of HVS. To derive the parameters of the spatial CSF, a series of subjective experiments is conducted to obtain the just-noticeable distortion (JND) thresholds of several selected DCT subbands. In addition, the sensitivities of different DCT subbands in one color channel, as well as among R, G, and B channels, are considered to design the WQMs of intra-coded 8 × 8 blocks. Moreover, to reduce the data size of WQMs, the WQMs for other block sizes are derived from intra 8 × 8 WQMs. The proposed WQMs are then applied into HEVC to directly code RGB videos. The experimental results demonstrate that when the PSNRs of G, B, and R channels are combined with a ratio of 4:1:1, the proposed WQMs can achieve an average BD-rate saving of 12.64% and 20.51%, respectively, in all-intra (AI) and low-delay (LD) profiles compared to HEVC without WQMs. The proposed scheme also enjoys a better video quality metric (VQM) performance.
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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 | 0.001 | 0.003 |
| Open science | 0.001 | 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".