A Novel No-Reference HD Video Quality Metric Based on Perceptual Temporal Pooling
Bibliographic record
Abstract
Impressive advancements in capturing, display, and broadcasting technologies significantly elevate image and video quality, and with that the need for designing new reference and no-reference image and video quality metrics. One of the latest and perceptually accurate video quality metrics is the Video Multi-Method Assessment Fusion (VMAF) method. However, VMAF considers the temporal nature of video using basic average temporal pooling, an approach that falls short from human perception. In this paper, we introduce a new no-reference video quality metric that uses deep learning to extract spatial features and a unique temporal pooling approach to accurately predict the visual quality score. To this end, first we created a video quality dataset that consists of high-resolution, 20s-long test video clips compressed at several different bitrates. These videos were labeled based on subjective evaluations and were used to determine the perceptual importance of frames in our temporal pooling scheme. Evaluations showed that our proposed approach achieved correlation of 90.55% with human perception and outperformed the state-of-the-art VMAF approach by 15.63% accuracy.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".