dc.contributor |
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.contributor |
Boulgouris, Nikolaos |
dc.contributor.author |
Arévalo López, Raúl |
dc.date |
2010-08 |
dc.identifier.uri |
http://hdl.handle.net/2099.1/10752 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.rights |
Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::So, imatge i multimèdia::Creació multimèdia::Imatge digital |
dc.subject |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject |
Video |
dc.subject |
Imatges -- Compressió (Informàtica) |
dc.subject |
Vídeo |
dc.title |
Compressive Sensing and Combinatorial algorithms for image compression |
dc.type |
info:eu-repo/semantics/bachelorThesis |
dc.description.abstract |
Projecte final de carrera fet en col.laboració amb King's College London |
dc.description.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. |