Modeling of the DCT coefficients of images
Bibliographic record
Abstract
In this paper, the symmetric normal inverse gaussian (SNIG) probability density function (PDF) is proposed as a highly suitable prior for modelling the DCT coefficients of natural images. A new method, based on minimizing the Kullback-Leibler divergence between the proposed prior and the empirical PDF extracted from image data, is proposed to estimate the SNIG parameters. The efficacy of the proposed parameter estimation technique is tested using Monte-Carlo simulations. It is shown that the SNIG PDF is a more effective prior as compared to the generalized Gaussian (GG), α-stable, and Laplacian PDFs for modelling the full-frame DCT coefficients of natural images. For the block-DCT coefficients, the SNIG PDF is shown to be better than the GG and Laplacian PDFs, and comparable to the α-stable one, while incurring much less complexity for parameter estimation.
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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 | 0.001 | 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".