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Image Compression with Neural Networks
Teuer, Lukáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings. In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.
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Image Compression Using the Wavelet Transform
Přikryl, Lukáš ; Maršík, Lukáš (referee) ; Bařina, David (advisor)
The thesis discusses the image compression using the wavelet transform. The compression itself is done by EZW, SPIHT or EBCOT Tier1 algorithms. These algorithms are then compared to JPEG and JPEG2000 standards. Furthermore tiles, various wavelets and RGB and Y'CbCr color spaces were used to determine the influence on an image compression quality.
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Error Resilience Analysis for JPEG 2000
Kovalčík, Marek ; Klíma, Ondřej (referee) ; Bařina, David (advisor)
The aim of this thesis is to analyze modern image compression format of JPEG 2000. It analyzes the effect of error resilience mechanisms on image compression with different settings. The impact of using tag embedding to help repair damaged images or using compression modes to improve error resilience is examined here. Quality is evaluated by the PSNR metric that detects the similarity of compressed and reference file. Adding certain tags to the data stream or using certain compression modes should help secure the JPEG 2000 file against image reconstruction damage. To test this hipothesis, there was created a model that acidentally damage the compressed file and evaluate decompressed images. The Kakadu library, which provides efficient work with the JPEG 2000 format, is used for the work. The experimental data set consists of various photographs in uncompressed PPM format in smaller but also in higher resolutions. The result of this work is to find out which compression settings to use for which group of images to make the compression efficient and secure the best. The end of this thesis is devoted to comparison of error resilience of JPEG 2000 and CCSDS 122.0.
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Image Compression with Neural Networks
Teuer, Lukáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings. In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.
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JPEG 2000 Implementation
Zlatohlávek, Adam ; Klíma, Ondřej (referee) ; Bařina, David (advisor)
The aim of this thesis is to propose a image compression methods in JPEG 2000. This consists of description of techniques used in base level stadard and analyze options in encoding process. The goal is to create compression process of input image from preprocessing to own output format. In the end of paperwork are presented results of own implementation in memory consuptions and encoding performance.
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Image Compression Using the Wavelet Transform
Bradáč, Václav ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This work deals with image compression using wavelet transformation. At the beginning , you can find theoretical information about the best known techniques used for image compression , a thorough description of wavelet transormation and the EBCOT algorithm. A significant part of the work is devoted to the library's own implementation . Another chapter of the diploma thesis deals with the comparison and evaluation of the achieved results of the processed library with the JPEG2000 format
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Lossy Image Compression
Bařina, David ; Herout, Adam (referee) ; Sumec, Stanislav (advisor)
This thesis deals with advanced methods of lossy image compression. To the midst of these methods belong the discrete cosine transform and Haar's discrete wavelet transform. Furthermore principles of color models and algorithms used in lossless data compression are explained in this text. The purpose of this thesis is the library implementation that is based on these cognizances.
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Medical Image Data Compression
Šebek, Jiří ; Bařina, David (referee) ; Kršek, Přemysl (advisor)
This thesis deals with several lossless methods of image compression. The main goal is to achieve the best compression ratio for set of medical images. This paper provides brief introduction into the data compression theory. It also contains design of image compression and description of designed compression modules implementation. Also the verifi cation of suitability of designed method for medical images compression is involved.
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Image Compression Using the Wavelet Transform
Přikryl, Lukáš ; Maršík, Lukáš (referee) ; Bařina, David (advisor)
The thesis discusses the image compression using the wavelet transform. The compression itself is done by EZW, SPIHT or EBCOT Tier1 algorithms. These algorithms are then compared to JPEG and JPEG2000 standards. Furthermore tiles, various wavelets and RGB and Y'CbCr color spaces were used to determine the influence on an image compression quality.
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Image compression in interactive applications in digital video broadcasting
Bodeček, Kamil ; Vrba, Kamil (advisor)
Compressed images are used very frequently in interactive applications in digital video broadcasting. New methods increasing efficiency of the image transmission in digital video broadcasting networks are proposed. Adaptive spatial filtering methods have been proposed for enhancement of the visual perception of the compressed images. New optimalization method is based on application of the filtering algorithms on more compressed images (data size are reduced). Visual quality enhancement is processed in interactive application. Further, new compression methods JPEG2000 and H.264 for image compression have been analysed. Novel compound image compression method for standard and high spatial television resolution is proposed in the thesis.
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