An adaptive fractal-based algorithm for image compression
Bibliographic record
Abstract
The use of fractal theory in the area of image compression is a relatively new and intriguing concept. Its theoretical basis is well established in the theory of iterated function systems, and particularly in partitioned iterated function systems. However, there are still numerous questions about its practical implementation to be answered. The main problem with this method is that of reducing the complexity of an otherwise very promising concept. We describe a simple and efficient adaptive fractal-based algorithm for image compression. The algorithm uses horizontal-vertical (HV) partitioning of an image into rectangular blocks of different sizes. The partitioning information is used in the encoding process for determining both the range and the domain image blocks. Neither the ranges nor the domains are determined in advance. Instead, the image is fully partitioned into small areas not larger than some predetermined size. The ranges and the corresponding domains are then determined in an adaptive manner, by comparing the rectangular image blocks with different scales. The proposed algorithm attempts to find a good cover for the ranges as large as possible and it then proceeds toward smaller ranges only if an optimal cover is not found with the larger scale. The method allows the total number of finally chosen ranges to be reduced, which is an essential requirement for achieving high compression ratios. The proposed algorithm gives roughly one-half the number of ranges compared to that given by the quad-tree based partitions yielding significant improvement in the compression ratio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".