Bibliographic record
Abstract
Wavelets have been successfully used in image compression. However, for the given image, the choice of the wavelet to use is an important issue. In this paper, we propose to use the optimal wavelet for image compression, given the number of most significant wavelet coefficients to be kept. Simulated annealing is used to find the optimal wavelet for the given image to be compressed. In simulated annealing, we need a cost function to minimize. This cost function is defined as the mean square error between the decompressed image and the original image. We conduct some experiments in Matlab by using the test images Lena, MRIScan and Fingerprint. These images are available in WaveLab, developed by Donoho et al., at Stanford University. Experimental results show that this approach is better than the Daubechies-8 wavelet (D8) for image compression. In some cases, we get nearly 0.6 dB improvement over D8 by using the optimal wavelet. This indicates that the choice of the wavelet indeed makes a significant difference in image compression.
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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 | 0.001 |
| 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".