A novel context modeling scheme is presented for lossless image compression. First, each line in the input image is divided into 1 × N line segments, called processing unit (PU). Then, the statistical reference is evaluated in each PU, which reveals the randomness of pixels in the local image region. The context is designed based on both neighbor pixels and the statistical reference. Finally, each pixel is adaptively compressed based on the proposed context condition. In the experiment, the proposed scheme yields the comparable performance to the standard JPEG-LS [1], while the number of context conditions are decreased by 30%. Moreover, the proposed system outperforms H.264/AVC [2] and JPEG-XR [3] by 8.3% and 4.9%, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.