National Repository of Grey Literature 8 records found  Search took 0.01 seconds. 
Image Compression Using the Wavelet Transform
Urbánek, Pavel ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This thesis is focused on subject of image compression using wavelet transform. The first part of this document provides reader with information about image compression, presents well known contemporary algorithms and looks into details of wavelet compression and following encoding schemes. Both JPEG and JPEG 2000 standards are introduced. Second part of this document analyzes and describes implementation of image compression tool including inovations and optimalizations. The third part is dedicated to comparison and evaluation of achievements.
Data stream compression and decompression methods.
Makedonenko, Oleksandr ; Kaczmarczyk, Václav (referee) ; Valach, Soběslav (advisor)
Cílem tento práce je prostudovat metody bezztrátové komprese a zmenšit datový tok ve komunikačním kanále, prováděním bezztrátové algoritmu komprese, který může být použity na FPGA desce s teoretickým dosahem rychlosti 1Gbit/s.
Lossless Image Compression
Němec, Jaroslav ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This thesis deals with lossless image compression. You can nd all the process of assembling lossless image coder/decoder. There are described many predictors, color models and two entropy coders in this thesis. The results of thesis are compared and disscused with current lossless image format PNG at the end of thesis.
Adaptive data compression by neural networks
Kučera, Michal ; Přinosil, Jiří (referee) ; Koula, Ivan (advisor)
Point of the work is using of neural networks for the datecompression. This brings new possibilities as by lossless as lossy compression. Draft of a few compress algorithm show the behaviour, advantages and weak points of these systems. As the solution we use knowledge of the layered perceptron Network and we try by the change of the structure and subparameters to teach such network to compress the data, according to our entry requirement. These networks have also advantages, which are meanwhile impediment to the using practically. The goal of this is to try some algorithms, look into their characteristics and posibility of the using. Then propose next posibility solutions and upgrading of these algorithms.
Data stream compression and decompression methods.
Makedonenko, Oleksandr ; Kaczmarczyk, Václav (referee) ; Valach, Soběslav (advisor)
Cílem tento práce je prostudovat metody bezztrátové komprese a zmenšit datový tok ve komunikačním kanále, prováděním bezztrátové algoritmu komprese, který může být použity na FPGA desce s teoretickým dosahem rychlosti 1Gbit/s.
Lossless Image Compression
Němec, Jaroslav ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This thesis deals with lossless image compression. You can nd all the process of assembling lossless image coder/decoder. There are described many predictors, color models and two entropy coders in this thesis. The results of thesis are compared and disscused with current lossless image format PNG at the end of thesis.
Image Compression Using the Wavelet Transform
Urbánek, Pavel ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This thesis is focused on subject of image compression using wavelet transform. The first part of this document provides reader with information about image compression, presents well known contemporary algorithms and looks into details of wavelet compression and following encoding schemes. Both JPEG and JPEG 2000 standards are introduced. Second part of this document analyzes and describes implementation of image compression tool including inovations and optimalizations. The third part is dedicated to comparison and evaluation of achievements.
Adaptive data compression by neural networks
Kučera, Michal ; Přinosil, Jiří (referee) ; Koula, Ivan (advisor)
Point of the work is using of neural networks for the datecompression. This brings new possibilities as by lossless as lossy compression. Draft of a few compress algorithm show the behaviour, advantages and weak points of these systems. As the solution we use knowledge of the layered perceptron Network and we try by the change of the structure and subparameters to teach such network to compress the data, according to our entry requirement. These networks have also advantages, which are meanwhile impediment to the using practically. The goal of this is to try some algorithms, look into their characteristics and posibility of the using. Then propose next posibility solutions and upgrading of these algorithms.

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