A low-power subsample-based image compression algorithm for capsule endoscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an efficient sub-sample based image compression algorithm targeted to the endoscopic application. Endoscopic images are converted from RGB to YCgCo plane; the non-significant color components are then sub-sampled to obtain better compression ratio without heavily affecting the reconstruction quality. The algorithm uses simple integer-based Discrete Cosine Transform followed by a division-free quantization stage that results in low-cost implementation. The scheme is applied to both the traditional wide band images (WBI), as well as the narrow band images (NBI) for the performance assessment. The overall compression ratio and PSNR for the WBI and NBI are 84.53% and 82.36%, and 40.64 dB and 41.24 dB respectively. The hardware implementation is also presented that shows that the proposed scheme results in longer battery life compared to other existing schemes.
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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 | 0.002 |
| Open science | 0.001 | 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