Enhancing JPEG XL’s Weighted Average Predictor: Genetic Algorithm Optimization of Expanded Sub-Predictor Ensemble
Bibliographic record
Abstract
Lossless image compression relies heavily on prediction algorithms to reduce spatial redundancy before entropy coding. The JPEG XL standard employs a weighted average predictor that combines four sub-predictors with adaptive weighting; however, it uses fixed initial scaling factors regardless of the image content. This study introduces WOP8 (weighted optimization predictor for 8 sub-predictors), which extends the predictor diversity and optimizes initial weights using a genetic algorithm. Four additional predictors were incorporated—adaptive MED (JPEG-LS), enhanced adaptive median, Paeth (PNG), and GAP-based (CALIC)—forming an eight-predictor ensemble. A genetic algorithm with a population of 30 and 24 generations optimized the weight configurations by minimizing the compressed file size of the training data. Experiments were conducted on the Kodak and Tecnick datasets to evaluate performance and generalizability. The Kodak color dataset showed notable gains: with the weighted average predictor in isolation, WOP8 achieved a 0.24 BPP reduction (2.7% improvement) at high effort levels. Under standard JPEG XL operation mode, improvements were minor but consistent. These results confirm the value of targeted predictor optimization and demonstrate that genetic algorithms can effectively discover dataset-specific weighting patterns, offering a foundation for future component-level enhancements in JPEG XL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".