Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Image File Formats: Past, Present, and Future
98
Zitationen
5
Autoren
2001
Jahr
Abstract
Despite the rapid growth of the Internet for storage and display of World Wide Web-based teaching files, the available image file formats have remained relatively limited. The recently developed portable networks graphics (PNG) format is versatile and offers several advantages over the older Internet standard image file formats that make it an attractive option for digital teaching files. With the PNG format, it is possible to repeatedly open, edit, and save files with lossless compression along with gamma and chromicity correction. The two-dimensional interlacing capabilities of PNG allow an image to fill in from top to bottom and from right to left, making retrieval faster than with other formats. In addition, images can be viewed closer to the original settings, and metadata (ie, information about data) can be incorporated into files. The PNG format provides a network-friendly, patent-free, lossless compression scheme that is truly cross-platform and has many new features that are useful for multimedia and Web-based radiologic teaching. The widespread acceptance of PNG by the World Wide Web Consortium and by the most popular Web browsers and graphic manipulation software companies suggests an expanding role in the future of multimedia teaching file development.
Ähnliche Arbeiten
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.852 Zit.
Pembrolizumab versus Chemotherapy for PD-L1–Positive Non–Small-Cell Lung Cancer
2016 · 9.974 Zit.
Gefitinib or Carboplatin–Paclitaxel in Pulmonary Adenocarcinoma
2009 · 8.220 Zit.
Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial
2015 · 6.490 Zit.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
2016 · 5.737 Zit.