Image compression using learned dictionaries by RLS-DLA and compared with K-SVD

The recently presented recursive least squares dictionary learning algorithm (RLS-DLA) is tested in a general image compression application. Dictionaries are learned in the pixel domain and in the 9/7 wavelet domain, and then tested in a straightforward compression scheme. Results are compared with...

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Vydáno v:2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) s. 1517 - 1520
Hlavní autoři: Skretting, Karl, Engan, Kjersti
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2011
Témata:
Dictionaries
dictionary learning
Discrete cosine transforms
Image coding
image compression
overcomplete dictionary
PSNR
RLS-DLA
sparse approximation
Training
Transform coding
Wavelet domain
ISBN:9781457705380, 1457705389
ISSN:1520-6149
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Shrnutí:The recently presented recursive least squares dictionary learning algorithm (RLS-DLA) is tested in a general image compression application. Dictionaries are learned in the pixel domain and in the 9/7 wavelet domain, and then tested in a straightforward compression scheme. Results are compared with state-of-the-art compression methods. The proposed compression scheme using RLS DLA learned dictionaries in the 9/7 wavelet domain per forms better than using dictionaries learned by other methods. The compression rate is just below the JPEG 2000 rate which is promising considering the simple entropy coding used.
ISBN:9781457705380
1457705389
ISSN:1520-6149
DOI:10.1109/ICASSP.2011.5946782