Lossless and reversible colour space transformation for Bayer colour filter array images
Bibliographic record
Abstract
We present two variants of a colour space transformation algorithm to encode Bayer colour filter array images that are based on integer coefficients; as a result, the algorithms are fully lossless and reversible in nature. These transformation algorithms are derived using an optimisation model that reduces the spectral redundancy of Bayer colour components, which results in lower prediction error variance and inter‐colour correlation. These methods, known as optimum reversible colour space transform (ORCT‐1 and ORCT‐2), improve the lossless bitrate of low complexity prediction model without using high complexity interpolation and inter‐colour prediction scheme. Extensive experimentation is performed using five sets of test images for different lossless compression algorithms: JPEG‐LS, JPEG‐2000 and JPEG‐XR. Experimental results show that, in all cases, the proposed schemes perform competitively with other methods with lower computational complexity, which makes them suitable for low‐cost imaging applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".