Characterizing Generalized Rate-Distortion Performance of Videos
Bibliographic record
Abstract
Rate-distortion (RD) analysis is at the heart of lossy data compression. Here we extend the idea to generalized RD (GRD) functions of compressed videos that characterize the visual quality of a video and its encoding profile, which includes not only bit rate but also other attributes such as video resolution. We first define the theoretical functional space of the GRD function by analyzing its mathematical properties. We show that the GRD function space is a convex set in a Hilbert space, inspiring a computational model of the GRD function, based on which a general framework is proposed for GRD function reconstruction from known samples. We collect a large-scale database of GRD functions generated from diverse video contents and encoders. Using the database, we demonstrate that real-world GRD functions are clustered in a low-dimensional subspace in the theoretical space of all possible GRD functions. Combining the GRD reconstruction framework and the learned low-dimensional space, we create a low-parameter eigen GRD (eGRD) method to accurately estimate the GRD function of a source video content from only a few queries. Experimental results show that the proposed algorithm significantly outperforms state-of-the-art empirical RD estimation methods in accuracy and efficiency. Finally, we demonstrate the usefulness of the proposed eGRD model in a practical application: video codec comparison.
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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".