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Title:Izgubno stiskanje slik z variacijskim avtokodirnikom
Authors:Leber, Žiga (Author)
Strnad, Damjan (Mentor) More about this mentor... New window
Kohek, Štefan (Co-mentor)
Files:.pdf MAG_Leber_Ziga_2018.pdf (1,38 MB)
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu preučujemo izgubno stiskanje slik z uporabo variacijskega avtokodirnika. Implementirali smo njegovo povratno različico, imenovano konvolucijski DRAW, ki v vlogi kodirnika in dekodirnika uporablja nevronsko mrežo LSTM. Za implementacijo smo uporabili jezik Python in knjižnico PyTorch. Delovanje algoritma smo testirali na podatkovni zbirki CIFAR-10 ter rezultate primerjali z metodo JPEG. Ugotovili smo, da so rezultati primerljivi v smislu kakovosti rekonstrukcije.
Keywords:stiskanje slik, nevronske mreže, variacijski avtokodirnik, konvolucijski DRAW
Year of publishing:2018
Publisher:Ž. Leber
COBISS_ID:21878294 Link is opened in a new window
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Categories:KTFMB - FERI
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Secondary language

Title:Lossy image compression using variational autoencoder
Abstract:In this thesis we study lossy image compression using variational autoencoder. We implemented its recurrent variant called convolutional DRAW, which uses a LSTM neural network in the role of the encoder and the decoder. The implementation was done in Python using the PyTorch library. The performance was tested the CIFAR-10 dataset and the results compared to the JPEG compression method. We determined that the results are comparable in reconstruction quality.
Keywords:image compression, neural networks, variational autoencoder, convolutional DRAW


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