Colour quantisation using self-organizing migrating algorithm
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Bibliographic record
Abstract
Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Selecting these colour, which comprise a colour palette, is a challenging task since they determine the resulting image quality. In this paper, we propose a novel colour quantisation algorithm based on the Self-Organizing Migrating Algorithm (SOMA), in particular, SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA. SOMA T3A works, iteratively in three phases, namely organization, migration, and update, and performs adaptive parameter definition. Migrants are selected from the population and move towards a leader during the organization process. Experimental results on a benchmark set of images show excellent colour quantisation performance and our approach to outperform several conventional and soft-computing-based colour quantisation algorithms.
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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 | 0.000 | 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 it