Contourlet based image compression using controlled modification of coefficients
Why this work is in the frame
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Bibliographic record
Abstract
A new compression algorithm is proposed in this paper which uses the contourlet transform. Unlike contourlet-based non-linear approximation (NLA) compression algorithms, the proposed algorithm modifies the coefficients in a controlled manner. The modification is performed so that the difference between a modified coefficient and its original value is within a certain range. To achieve higher compression, the modifications are performed with the goal of minimizing the entropy of the coefficients. The implementation results show that our algorithm produces images with higher PSNRs, for similar bit-rate conditions, as compared to NLA compression algorithms. Furthermore, the visual quality of the images produced by our algorithm is higher than the mentioned NLA algorithms. The implementation results also show the superiority of our algorithm over WBCT algorithm which is based on the joint application of wavelet and contourlet transforms.
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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 | 0.000 | 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 it