Error modeling for hierarchical lossless image compression

The authors present a new method for error modeling applicable to the multi-level progressive (MLP) algorithm for hierarchical lossless image compression. This method, based on a concept called the variability index, provides accurate models for pixel prediction errors without requiring explicit transmission of the models. They also use the variability index to show that prediction errors do not always follow the Laplace distribution, as is commonly assumed; replacing the Laplace distribution with a more general distribution further improves compression. They describe a new compression measurement called compression gain, and give experimental results showing that the using variability index gives significantly better compression than other methods in the literature.<>