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Title:
Paralelizacija algoritmov diskretne kosinusne transformacije in VP8 na GPE za izgubno stiskanje slik : diplomsko delo
Authors:
ID
Vidovič, Domen
(
Author
)
ID
Lukač, Niko
(
Mentor
)
More about this mentor...
ID
Bizjak, Marko
(
Comentor
)
Files:
UN_Vidovic_Domen_2019.pdf
(1,59 MB)
MD5: DCF1DC3CD08328CFEE7E7952585B5F40
PID:
20.500.12556/dkum/c1bcd6cf-e283-4afb-a84d-26d7201af0a3
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu preučimo uporabo grafično procesne enote (GPE) za namen kompresije slik. Najprej teoretično zasnujemo algoritma VP8 in diskretno kosinusno transformacijo (DCT), nato predstavimo njuno osnovno implementacijo. Algoritem DCT še dodatno optimiziramo in oba algoritma paraleliziramo z uporabo GPE. Na koncu primerjamo hitrost obeh implementacij in ugotovimo kdaj je za stiskanje slik smiselno uporabiti GPE.
Keywords:
kompresija slik
,
paralelizacija
,
GPGPU
,
CUDA
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[D. Vodovič]
Year of publishing:
2019
Number of pages:
VI, 24 str.
PID:
20.500.12556/DKUM-74522
UDC:
004.925.8:528.852(043.2)
COBISS.SI-ID:
22860822
NUK URN:
URN:SI:UM:DK:4QLQHEE1
Publication date in DKUM:
12.11.2019
Views:
1416
Downloads:
83
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
29.08.2019
Secondary language
Language:
English
Title:
Parallelization of discrete cosine transform and VP8 algorithms on GPU for lossy image compression
Abstract:
In this thesis we study the use of graphics processing units (GPU) for image compression. We begin by theoretically defining algorithms VP8 and discrete cosine transform (DCT), then we present a basic implementation of the two algorithms. Additionaly, we optimize the DCT implementation and parallelize both algorithms to be used on a GPU. Finally, we compare the performance of both implementations and determine when using GPUs for image compression is suitable.
Keywords:
image compression
,
parallelization
,
GPGPU
,
CUDA
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