Comparison of JPEG 2000 and Other Lossless Compression Schemes for Digital Mammograms
Bibliographic record
Abstract
In this study, we propose JPEG 2000 as an algorithm for the compression of digital mammograms and the proposed work is the first real-time implementation of JPEG 2000 on a mammogram image database. Only the lossless compression mode of JPEG 2000 was examined to ensure that the mammogram is delivered without distortion. The performance of JPEG 2000 was compared against several other lossless coders: JPEG-LS, lossless-JPEG, adaptive Huffman, arithmetic with a zero order and a first order probability model and Lempel-Ziv Welch (LZW) with 12 and 15 bit dictionaries. Each compressor was supplied the identical set of 50 mammograms, each having a resolution of 8bits/pixel and dimensions of 1024 × 1024. Experimental results indicate JPEG 2000 and JPEG-LS provide comparable compression performance since their compression ratios differed by 0.72% and both compressors also superseded the results of the other coders. Although JPEG 2000 suffered from a slightly longer encoding and decoding delay than JPEG-LS (0.8s on average), it is still preferred for mammogram images due to the wide variety of features that aid in reliable image transmission, provide an efficient mechanism for remote access to digital libraries and contribute to fast database access.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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 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".