期刊
APPLIED SCIENCES-BASEL
卷 12, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/app12052421
关键词
sparse coding; orthogonal sparse coding; dictionary learning; image transform; sparse orthonormal transform
类别
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [1711108458]
This paper proposes an extension of a sparse orthonormal transform based on unions of orthonormal dictionaries for image compression. The method constructs dictionaries into a discrete cosine transform and an orthonormal matrix, and adapts Bayesian optimization to determine a trade-off parameter for efficient implementation and optimal parameter selection.
Sparse orthonormal transform is based on orthogonal sparse coding, which is relatively fast and suitable in image compression such as analytic transforms with better performance. However, because of the constraints on its dictionary, it has performance limitations. This paper proposes an extension of a sparse orthonormal transform based on unions of orthonormal dictionaries for image compression. Unlike unions of orthonormal bases (UONB), which implement an overcomplete dictionary with several orthonormal dictionaries, the proposed method allocates patches to an orthonormal dictionary based on their directions. The dictionaries are constructed into a discrete cosine transform and an orthonormal matrix. To determine a trade-off parameter between the reconstruction error and sparsity, which hinders efficient implementation, the proposed method adapts Bayesian optimization. The framework exhibits an improved performance with fast implementation to determine the optimal parameter. It is verified that the proposed method performs similar to an overcomplete dictionary with a faster speed via experiments.
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