JNTD: Toward Just Noticeable Frame Rate-Based Temporal Difference for Perceptual Video Coding
Bibliographic record
Abstract
Just Noticeable Difference (JND) refers to the maximum level of distortion in an image or video sequence that remains imperceptible to the Human Visual System (HVS). Current JND-based studies predominantly rely on existing datasets, developing models predicting JND levels in terms of Quantization Parameter (QP) or Quality Factor (QF). However, these solutions primarily focus on spatial-based Perceptual Video Coding (PVC) and neglect temporal-based optimization, which highly affects the video bitrate. This paper addresses this limitation by introducing Just Noticeable frame rate-based Temporal Difference (JNTD) to determine the optimal Frame Rate (FR) based on human perception. A novel dataset comprising 50 high frame rate video sequences is collected through subjective assessments. Subsequently, an ensemble method is proposed to predict the JNTD, by leveraging deep and hand-crafted features, for robust prediction. Experimental evaluations include the integration of the proposed method into several codecs (H.264, H.265, H.266, and a new learned codec), showcasing its ability to reduce bitrate without compromising visual quality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".