(A) study on learning-based linear and nonlinear sparsifying transforms for image compression = 영상 압축을 위한 학습 기반 선형 및 비선형 희소 변환에 관한 연구

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Recently, active research has been made on sparse representation of images and video for data compression. The transforms often used to compress images or video include DCT (discrete cosine transform) and Wavelet transforms. Since these transforms concentrate most of the energy of images into a small number of low frequency coefficients, the compression performance can be greatly increased. Recently, there have been studies to improve the compression performance by learning a transform or a dictionary so as to be more suitable for specific data. These studies enable a more sparse representation than the existing transforms for certain data. In this thesis, we study how to learn sparsifying transforms for directionally predicted pixel blocks of H.264/AVC and to compare them with the existing linear transform models. Furthermore, by interpreting the above linear transform models as a neural network with one layer, we extend them to nonlinear sparsifying transforms based on neural networks with multiple layers to obtain more sparsifying transforms. We compare the nonlinear sparsifying transforms with the linear sparsifying transforms in terms of compact representation capability.
Advisors
Kim, Munchurlresearcher김문철researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[iii, 37 p. :]

Keywords

Sparsifying Transform; Image Compression; Transform Learning; Neural Network; KLT; 희소 변환; 이미지 압축; 변환 학습; 신경망; 카루넨 루베 변환

URI
http://hdl.handle.net/10203/243276
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675382&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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