Quality-aware video based on robust embedding of intra- and inter-frame reduced-reference features
Bibliographic record
Abstract
With the rapid development of network visual communications, there is an urgent need of effective and efficient video quality assessment (VQA) methods for quality control and resource allocation purposes. In this paper, a spatial and temporal reduced-reference (RR) VQA measure is combined with a robust video watermarking approach, leading to a quality-aware video (QAV) system. At the sender side, both intra- and inter-frame RR features are calculated from the original video based on statistical models of natural video. This is followed by error control coding to improve robustness. The encoded features are then embedded invisibly into the same video signal using a robust angle quantization index modulation based watermarking method in 3D discrete cosine transform domain. At the receiver side, the RR features are extracted and decoded from the distorted video and employed to predict the perceptual degradation of the video signal. Experimental results demonstrate the applicability of the proposed approach to a wide range of distortion types and levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".