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论文类型：会议论文

发表时间：2017-01-01

收录刊物：CPCI-S

卷号：2017-December

页面范围：217-224

关键字：sparse representation; image set compression; dictionary learning; image coding

摘要：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.