Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

Abstract

Image set compression has recently become an active research topic due to the explosion of digital photographs. In order to efficiently compress the image sets of similar images with moving objects, in this paper, we propose a novel algorithm for image set compression using multiple reference images. First, for an image set, its depth-constrained minimum arborescence is generated. We then present a reference image candidate determination method to build the reference image candidates for the images of the set. Furthermore, we propose a rate-distortion optimized multiple reference image selection method. This method compares the correlation between every image and each of its reference image candidates to produce its multiple reference images. Finally, compressed image data are achieved by employing block-based motion compensation and residue coding. In addition, we also give a new way of access to images to keep the same access delay with single reference image-based schemes. Compared with the state-of-the-art image compression algorithms, experimental results show that our proposed algorithm can significantly improve the image compression performance.