Image Compression of medical images using VQ-Huffman Coding Technique
Bibliographic record
Abstract
Digital Medical Imaging has grown very fast in recent years and hence plays a vital role in diagnosis, treatment, and research area. All the radiological modalities such as CT scanners, MRI, US, PET, X-Ray made by multiple vendors and located at one or many sites can communicate by means of DICOM across an network. Now days, hospitals need to store large volume of data about the patients that require huge hard disk space and high bandwidth. This would employ the need to compress DICOM images for efficient storage and transmission over the internet. In this paper, a new compression algorithm combining the features of both lossy (DCT) and lossless (Huffman Coding) compression techniques has been designed and implemented. The performance of proposed algorithm is then improved using Vector Quantization technique in the context of increasing Compression Ratio as well as preserving the quality of compressed images. Different quality metrics like MSE, PSNR and CR are computed on various medical test images. The experimental results show that proposed compression technique performs better than the existing techniques in terms of performance parameters.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".