Lossless Compression in Bayer Color Filter Array for Capsule Endoscopy
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a compression algorithm for color filter array (CFA) images in a wireless capsule endoscopy system. The proposed algorithm consists of a new color space transformation (known as YLMN), a raster-order prediction model, and a single context adaptive Golomb-Rice encoder to encode the residual signal with variable length coding. An optimum reversible color transformation derivation model is presented first, which incorporates a prediction model to find the optimum color transformation. After the color transformation, each color component has been independently encoded with a low complexity raster-order prediction model and Golomb-Rice encoder. The algorithm is implemented using a TSMC 65-nm CMOS process, which shows a reduction in gate count by 38.9% and memory requirement by 71.2% compared with existing methods. Performance assessment using CFA database shows the proposed design can outperform existing lossless and near-lossless compression algorithms by a large margin, which makes it suitable for capsule endoscopy application.
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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 | 0.001 | 0.004 |
| Open science | 0.006 | 0.001 |
| 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