Comparative study of the spectral selection based on image compression methods
Bibliographic record
Abstract
Objectives: To investigate the image compression methods based on spectral selection and to carry out a comparative study. Methods: The technique consists of comparing pixel by pixel the spectra of the images being compressed. To identify the winner among the spectral pixels, we investigated three forms of information, namely the intensity of the pixel in question, its complex gradient and its phase gradient. To separate the images in the output plane, after applying a second Fourier transform, the retained pixel must integrate the carrier specific to the corresponding spectrum. In addition to these forms, we considered three options of selection, namely the selection of the pixel with the most important amount of information, the selection with respect to a fixed probability function and finally the selection based on a matched probability function. This leads to nine methods of compression. These methods were also compared to the method which consists in merely adding the spectra and inserting a carrier specific to each one. Results: the quality of image reconstitution are assessed by two metrics namely diffraction efficiency and root mean square error. We observed that for any form of information on the basis of which the selection is performed, the matched probability function yields a better result. The methods based on the gradients privilege contour information. Conclusions and perspectives: Whatever the retained method is, the number of images to be compressed is very limited. It depends on the bandwidths of the images. In perspective, we plan to integrate the bandwidth in the spectral selection. The binarization of the obtained spectrum will be also the subject of a future work.
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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".