Lossless image compression with tree coding of magnitude levels

With the rapid development of digital technology in consumer electronics, the demand to preserve raw image data for further editing or repeated compression is increasing. Traditional lossless image coders usually consist of computationally intensive modeling and entropy coding phases, therefore might not be suitable to mobile devices or scenarios with a strict real-time requirement. This paper presents a new image coding algorithm based on a simple architecture that is easy to model and encode the residual samples. In the proposed algorithm, each residual sample is separated into three parts: (1) a sign value, (2) a magnitude value, and (3) a magnitude level. A tree structure is then used to organize the magnitude levels. By simply coding the tree and the other two parts without any complicated modeling and entropy coding, good performance can be achieved with very low computational cost in the binary-uncoded mode. Moreover, with the aid of context-based arithmetic coding, the magnitude values are further compressed in the arithmetic-coded mode. This gives close performance to JPEG-LS and JPEG2000.