Implantable narrow band image compressor for capsule endoscopy
Bibliographic record
Abstract
A low-complexity narrow band image compressor for wireless capsule endoscopy application is presented in this paper. The compression algorithm is based on a simplified RGB-YUV color space converter, differential pulse coded modulation (DPCM) followed by optimized Golomb-Rice coding. Based on the nature of the narrow band endoscopic images, several sub-sampling schemes on the chrominance components are applied. The compressor does not need any buffer memory and can be interfaced with image sensors which send pixels in raster-scan fashion. The proposed algorithm has compression ratio of 81% and high reconstruction peak-signal-to-noise-ratio , over 40 dB. The proposed compressor is implemented in 0.18μm CMOS technology and consumes 2K standard gates, 0.67 mm × 0.71 mm silicon area, and 42 μW of power when working at 2 frames-per-second.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".