diplomsko delo
Jan Pelicon (Author), Luka Čehovin (Mentor)

Abstract

Diplomsko delo obravnava video kompresijo z uporabo nevronskih mrež. V zadnjih letih se je namreč z napredkom strojnega učenja pojavila ideja, da bi se kompresijo slikovnih in video podatkov lahko naučili z ustrezno arhitekturo nevronske mreže in veliko količino učnih podatkov. V nalogi smo se osredotočili na uporabo konvolucijskih samokodirnikov, ki slikovne podatke iz vhodnega prostora preslikajo v bolj kompakten latentni prostor ter nazaj. Predstavimo dva pristopa za kompresijo podatkov, prvi ima za cilj zgolj kompresijo posameznih slik, drugi pa predstavlja nadgradnjo v smeri video kompresije, ki sledi klasičnemu pristopu napovedovanja gibanja delov slike ter kodiranju popravkov. Opisali smo uporabljene arhitekture ter postopek učenja in testiranja. Več pozornosti smo posvetili operaciji kvantizacije, ki je pomemben element preko katerega kontroliramo nivo kompresije in kvaliteto rekonstrukcije. Testirali smo osnovno implementacijo in primerjali zmogljivost v primerjavi z JPEG formatom. Za testiranje druge implementacije smo si izbrali dve konfiguraciji, ju testirali pri različnih parametrih in primerjali s standardnimi kodeki za video kompresijo. Čeprav sta oba pristopa učinkovito kompresirala podatke, nista dosegala trenutnih standardov, zato predstavimo možne izboljšave, s katerimi bi se približali trenutnim standardom.

Keywords

video kompresija;konvolucijske nevronske mreže;samokodirniki;kompresijsko ogrodje;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [J. Pelicon]
UDC: 004.8(043.2)
COBISS: 30808835 Link will open in a new window
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Downloads: 189
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Other data

Secondary language: English
Secondary title: Video compression using neural networks
Secondary abstract: This bachelor's thesis uses neural networks to compress video. Due to improvements in deep learning, a new idea appeared. Neural networks can learn to compress image and video data using large training sets and appropriate architecture. In the thesis, we used convolutional autoencoders that can transform input data into smaller latent space. We present two approaches to compression. The first one is designed to compress images, while the second is improved to compress video material. It is based on the classic approach of predicting movement in a scene and has error correction. We described used architectures and processes of learning and testing. We focused more on a quantization operation which is an important element for controlling compression ratio and quality. We evaluated the first approach and compared it with the JPEG image compression format. We chose two different configurations for the second approach, tested them using multiple parameters, and compared results with performances of standard codecs. Although both approaches are capable of efficient compression, they can not compete with today's standards. Because of this, we also mentioned some novelties that could significantly improve performance.
Secondary keywords: video compression;convolutional neural networks;autoencoders;compression framework;computer and information science;diploma thesis;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 84 str.
ID: 12033204