Improved Low Complexity Predictor for Block-Based Lossless Image Compression

Lossless image compression has been studied and widely applied, particularly in medicine, space exploration, aerial photography, and satellite communication. In this study, we proposed a low-complexity lossless compression for image (LOCO-I) predictor based on the joint photographic expert group–lossless standard (JPEG-LS). We analyzed the nature of the LOCO-I predictor and offered possible solutions. The improved LOCO-I outperformed LOCO-I by a reduction of 2.26% in entropy for the full image size and reductions of 2.70, 2.81, and 2.89% for 32 × 32, 16 × 16, and 8 × 8 block-based compression, respectively. In addition, we suggested vertical/horizontal flip for block-based compression, which requires extra bits to record and decreases the entropy. Compared with other state-of-the-art (SOTA) lossless image compression predictors, the proposed method has low computation complexity as it is multiplication- and division-free. The model is also better suited for hardware implementation. As the predictor exploits no inter-block relation, it enables parallel processing and random access if encoded by fix-length coding (FLC).