Low complexity lossless image compression using efficient context modeling

Sung-Bum Park, Jung-Woo Kim, Dai-Woong Choi, Jae-Won Yoon, Jae-Hyun Kim

Abstract: 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, while the number of context conditions are decreased by 30%. Moreover, the proposed system outperforms H.264/AVC and JPEG-XR by 8.3% and 4.9%, respectively.