Bibliographic record
Abstract
This paper examines the process of image denoising to improve the efficiency of the reduced-search fractal block coding (FBC) of greyscale images by reducing the first-order entropy of the image. The reduced-search FBC is a lossy compression technique that exploits the block-wise self-affinity of an image where portions of the image are represented by scaled and isometrically transformed copies of other portions of the image. The efficiency of this process increases with increased redundancy which is the result of lowering the entropy. Image denoising is concerned with separating noise from an image and then suppressing the noise as much as possible without altering the image itself. In this paper spatial smoothing and wavelet denoising are compared. It is shown that denoising increases the efficiency of reduced-search FBC. Spatial smoothing, however, causes a loss of signal that wavelet denoising does not. In either case, the reconstruction qualities of the peak-signal-to-noise ratio at approximately 34 dB and compression ratios of 18.9:1 and higher have been achieved. This is an improvement over the 31 dB and 18.1:1 for non-denoised images.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".