Compressive Sensing and Combinatorial algorithms for image compression
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099.1/10752
Tutor / directorBoulgouris, Nikolaos
Tipus de documentProjecte/Treball Final de Carrera
Data2010-08
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
The initial motivation of this Masters Thesis is the design and analysis of an image
compression method based on Compressive Sensing. Compressigve Sensing is a technique
which allows coding sparse signals by projecting the signal onto random vectors. When
signals are sparse, it is possible to encode the signal with a much smaller number of
measurements than the length of the original signal.
Two methods based on Compressive Sensing are proposed. Both of them initially
apply a wavelet transform to obtain the signal in a convenient domain in which is supposed to
be sparse. At the same time, in the wavelet transform domain some sub-blocks are generated,
which are useful in order not to process the whole image, but sub-block per sub-block. The
first method uses compressive sensing onto binary signals and the second one onto integers.
By studying and testing the proposed methods, a new one not based on compressive
sensing emerged which provided significant improvements. It is a combinatorial method
which orders uniquely the possible combinations of a binary vector of length N with S nonzero
coefficients. It is supported by a fast algorithm to cycle through the combinations.
Results provided in this report allow having an idea of the efficiency and advantages
and disadvantages of each one of the methods proposed.
Descripció
Projecte final de carrera fet en col.laboració amb King's College London
TitulacióENGINYERIA DE TELECOMUNICACIÓ (Pla 1992)
Fitxers | Descripció | Mida | Format | Visualitza |
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Raúl Arévalo- C ... Combinatorial algori….pdf | 1,304Mb | Visualitza/Obre |