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Improving image quality by SSIM based increase of run-length zeros in GPGPU JPEG encoding
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
2014 (engelsk)Inngår i: Conference Record of the Asilomar Conference on Signals Systems and Computers, IEEE Computer Society, 2014, s. 1714-1718Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

JPEG encoding is a common technique to compress images. However, since JPEG is a lossy compression certain artifacts may occur in the compressed image. These artifacts typically occur in high frequency or detailed areas of the image. This paper proposes an algorithm based on the SSIM metric to improve the experienced quality in JPEG encoded images. The algorithm improves the quality in detailed areas by up to 1.29 dB while reducing the quality in less detailed areas of the image, thereby increasing the overall experienced quality without increasing the image data size. Further, the algorithm can also be used to decrease the file size (by up to 43%) while preserving the experienced image quality. Finally, an efficient GPU implementation is presented. © 2014 IEEE.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2014. s. 1714-1718
Serie
Conference Record of the Asilomar Conference on Signals Systems and Computers, ISSN 1058-6393
Emneord [en]
Algorithms; Encoding (symbols); Image coding; Image quality; Program processors, Compressed images; File sizes; GPU implementation; High frequency HF; Image data; Lossy compressions; Run length, Image compression
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-10673DOI: 10.1109/ACSSC.2014.7094760ISI: 000370964900309Scopus ID: 2-s2.0-84940492806ISBN: 978-1-4799-8297-4 (tryckt)OAI: oai:DiVA.org:bth-10673DiVA, id: diva2:854345
Konferanse
48h Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA
Prosjekter
BigData@BTH - Scalable resource-efficient systems for big data analytics
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2015-09-16 Laget: 2015-09-15 Sist oppdatert: 2018-02-02bibliografisk kontrollert

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Totalt: 133 treff
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