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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:CPCI-S

Volume:2017-December

Page Number:217-224

Key Words:sparse representation; image set compression; dictionary learning; image coding

Abstract:Image compression plays more and more important role in image processing. Image sparse coding with learned over-complete dictionaries shows promising results on image compression by representing images with dictionary atoms compactly. Within the sparse coding based compression framework, a sparse dictionary is first learned from training images in a predefined image library, and then an image is compressed by representing its non-overlapping image patches as linear combination of very few dictionary atoms, which is called sparse coding. In this paper, we proposed a content adaptive sparse dictionary for image set compression based on sparse coding. For a set of similar images to be compressed, first we divided image patches into DC and AC components. For the AC components, a clustering algorithm is used to get cluster centers. Then a content adaptive dictionary will be learned according to each cluster center. We compared our method with RLS-DLA method and JPEG method to validate the performance of our method, and experimental results show that our method outperforms the comparing methods at high bitrate.