Colour‐reproduction algorithm for transmitting variable video frames and its application to capsule endoscopy
Bibliographic record
Abstract
Presented is a new power-efficient colour generation algorithm for wireless capsule endoscopy (WCE) application. In WCE, transmitting colour image data from the human intestine through radio frequency (RF) consumes a huge amount of power. The conventional way is to transmit all R, G and B components of all frames. Using the proposed dictionary-based colour generation scheme, instead of sending all R, G and B frames, first one colour frame is sent followed by a series of grey-scale frames. At the receiver end, the colour information is extracted from the colour frame and then added to colourise the grey-scale frames. After a certain number of grey-scale frames, another colour frame is sent followed by the same number of grey-scale frames. This process is repeated until the end of the video sequence to maintain the colour similarity. As a result, over 50% of RF transmission power can be saved using the proposed scheme, which will eventually lead to a battery life extension of the capsule by 4-7 h. The reproduced colour images have been evaluated both statistically and subjectively by professional gastroenterologists. The algorithm is finally implemented using a WCE prototype and the performance is validated using an ex-vivo trial.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".