Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data Reduction Methods for Deep Images
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Computer science.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Deep images for use in visual effects work during deep compositing tend to be

very large. Quite often the files are larger than needed for their final purpose,

which opens up an opportunity for optimizations. This research project is

about finding methods for identifying redundant and excessive data use in

deep images, and then approximate this data by resampling it and representing

it using less data. Focus was on maintaining the final visual quality while

optimizing the files so the methods can be used in a sharp production

environment. While not being very successful processing geometric data, the

results when optimizing volumetric data were very succesfull and over the

expectations.

Place, publisher, year, edition, pages
2017. , p. 48
Keyword [en]
deep images, deep compositing, data reduction, optimization, resampling, reduction, collapsing, file size, compositing, visual effects, film effects
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hig:diva-25473OAI: oai:DiVA.org:hig-25473DiVA, id: diva2:1153563
Subject / course
Computer science
Educational program
Creative programming
Supervisors
Examiners
Available from: 2017-12-06 Created: 2017-10-30 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(87691 kB)44 downloads
File information
File name FULLTEXT01.pdfFile size 87691 kBChecksum SHA-512
2b22925b221db704e33d23c9398145a628ead645f925a60929519f8c9866a30dcbf3938afcd88aa6ac9450e4eb1655ccf7bc137e919f1d792a0f7b8a11e6460a
Type fulltextMimetype application/pdf

By organisation
Computer science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 44 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 385 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.34-SNAPSHOT
|