An improved YEF-DCT based compression algorithm for video capsule endoscopy
Why this work is in the frame
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Bibliographic record
Abstract
Video capsule endoscopy is a non-invasive technique to receive images of intestine for medical diagnostics. The main design challenges of endoscopy capsule are accruing and transmitting acceptable quality images by utilizing as less hardware and battery power as possible. In order to save wireless transmission power and bandwidth, an efficient image compression algorithm needs to be implemented inside the endoscopy electronic capsule. In this paper, an integer discrete-cosine-transform (DCT) based algorithm is presented that works on a low-complexity color-space specially designed for wireless capsule endoscopy application. First of all, thousands of human endoscopic images and video frames have been analyzed to identify special intestinal features present in those frames. Then a color space, referred as YEF, is used. The YEF converter is lossless and takes only a few adders and shift operation to implement. A low-cost quantization scheme with variable chroma sub-sampling options is also implemented to achieve higher compression. Comparing with the existing works, the proposed transform coding based compressor performs strongly with an average compression ratio of 85% and a high image quality index, peak-signal-to-noise ratio (PSNR) of 52 dB.
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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 | 0.001 |
| Open science | 0.002 | 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