Image compression through optimized linear mapping and parametrically generated features
Bibliographic record
Abstract
In this paper a new linear mapping scheme for image compression is proposed. The main objective is to construct an asymptotic approximation to higher order surface fitting schemes previously reported in [1]. Each block of the image is independently reconstructed from a set of "features" through a linear mapping. These features should be (ideally) independent or at least uncorrelated to benefit the info-max principle. A random sequence generator is employed, using a sine function with two parameters, to approach such a requirement for the features. These two parameters and the set of linear weights are found through an optimization process. An off-line training phase is first performed to find the weights used in the linear mapping. These weights are then used to compress images during the on-line phase where the sine function parameters are found and quantized. The computation time is excessive due to nonlinear optimization required. However, thanks to quantization, a look-up table can be implemented to overcome this disadvantage. The proposed structure is fairly robust to the random sequence length as experimentally demonstrated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".