Rate-Distortion-Perception Tradeoff Based on the Conditional Perception Measure
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Bibliographic record
Abstract
In this paper, we study the rate-distortion-perception tradeoff generalizing the classical rate-distortion theory by adding a perception constraint to generate visually pleasing reconstructions. The perception metric measures the divergence between the distributions of the input and the reconstruction when both distributions are conditioned on the encoder's output. This metric, originally introduced by Mentzer et al. for the video compression setting, is called as conditional perception measure. We characterize the rate-distortion-perception tradeoff for a general source. In the Gaussian setting, we show that jointly Gaussian reconstructions are indeed optimal. Interestingly, to achieve a perceptually perfect reconstruction, comparing to the minimum mean square error (MMSE) reconstruction, we only need extra 0.5 bits/sample for the compression rate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it