Improving Medical Video Coding Using Multi Scale Quincunx Lattice: From Low Bitrate to High Quality
Bibliographic record
Abstract
In recent years, the healthcare sector has seen an increase in the use of medical images and videos. However, storage and transmission of this huge volume of data remain a challenging task, requiring the use of compression techniques. In this paper, the authors propose an algorithm to improve the visual quality of compressed medical video for lower bitrate without modifying the content of information such as edges and textures, this is a unique way for doctors to store and share medical data over the internet. The algorithm has not yet been sufficiently explored in medical video coding. In this study, the performances of the quincunx wavelet transform (QWT) combined with the set partitioning in hierarchical trees (SPIHT) encoder are discussed. The QWTs were chosen due to their limited number of wavelets family and reduced dilatation factor. The high efficiency of the suggested algorithm is checked against the coding standard based on the discrete cosines transform (DCT) or discrete wavelet transform (DWT). The assessment of the quality of the decoded video is based on the use of the peak signal to noise ratio (PSNR), the mean structural similarity (MSSIM) and the visual information fidelity (VIF). The results prove that the QWT+SPIHT provide competing performance where the PSNR reached 33 dB value for lower bitrate (137.408 Kbps) against previous standards.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".