Demo Data from EDBT 2026 / reviewers in the wild / expert
Loren Zaremba
dblp:321/5444
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1995
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Image and video coding · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video coding
lossless compression |
0.0 | 1 | 1995 | Radiologic image compression-a review · Proc. IEEE 1995 |
Image and video coding
lossy compression |
0.0 | 1 | 1995 | Radiologic image compression-a review · Proc. IEEE 1995 |
Image and video coding › image compression
medical image compression |
0.0 | 1 | 1995 | Radiologic image compression-a review · Proc. IEEE 1995 |
Medical and health informatics › radiology › radiology informatics
picture archiving and communication systems |
0.0 | 1 | 1995 | Radiologic image compression-a review · Proc. IEEE 1995 |
Image and video coding › quality assessment
compression quality assessment |
0.0 | 1 | 1995 | Radiologic image compression-a review · Proc. IEEE 1995 |
Methods — techniques the papers use, named apart from their topics
transform coding · 0.0lossy coding · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Radiologic image compression-a reviewabstractThe objective of radiologic image compression is to reduce the data volume of and to achieve a low bit rate in the digital representation of radiologic images without perceived loss of image quality. However, the demand for transmission bandwidth and storage space in the digital radiology environment, especially picture archiving and communication systems (PACS) and teleradiology, and the proliferating use of various imaging modalities, such as magnetic resonance imaging, computed tomography, ultrasonography, nuclear medicine, computed radiography, and digital subtraction angiography, continue to outstrip the capabilities of existing technologies. The availability of lossy coding techniques for clinical diagnoses further implicates many complex legal and regulatory issues. This paper reviews the recent progress of lossless and lossy radiologic image compression and presents the legal challenges of using lossy compression of medical records. To do so, we first describe the fundamental concepts of radiologic imaging and digitization. Then, we examine current compression technology in the field of medical imaging and discuss important regulatory policies and legal questions facing the use of compression in this field. We conclude with a summary of future challenges and research directions.> Loren Zaremba, David Gooden, H. K. Huang |
Proc. IEEE | 2 |