%0 Journal Article
%A FANG Meidong
%A WANG Hui
%A ZHANG Aihua
%T Fractal Image Compression Algorithm Based on Double-Layer Non-Negative Matrix Factorization
%D 2022
%R 10.3778/j.issn.1002-8331.2009-0350
%J Computer Engineering and Applications
%P 204-213
%V 58
%N 8
%X As a structure-based image compression technology, fractal image compression is used in many image processing. However, the encoding stage of fractal image compression is very time-consuming, and the quality of the reconstructed image is not good. To solve these problems, a fractal image compression coding algorithm based on double-layer non-negative matrix factorization（DLNMF） is proposed. In the traditional theory of non-negative matrix factorization（NMF）, the projection non-negative matrix factorization（PNMF） is combined with the [L3/2] norm constraint to extract representative image features in a short time. Firstly, the features of the original image are extracted by double-layer non-negative matrix decomposition; then the image features are clustered by [K]-means, and the classified image blocks are obtained according to the corresponding index; orthogonal sparse decomposition is performed in the corresponding class blocks to obtain the fractal code; and finally, the reconstructed image is obtained according to the fractal code. Experimental results show that compared with images reconstructed by fast sparse fractal image compression theory, the fractal compression algorithm of double-layer non-negative matrix factorization improves the quality of the reconstructed image and shortens the encoding time.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2009-0350