Image Set Compression with Content Adaptive Sparse Dictionary

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.