Abstract
This chapter includes the basic techniques used in lossless image compression so that the images are retrieved without any error or distortion. Usually, 8 bits are used to represent these values, hence the intensity values of a gray-scale image can vary from 0 to 255, whereas in radiology, the images are typically quantized to 12 bits. There is a brief introduction to the basic ideas used in lossless compression and the loss of data during entropy coding schemes, which result in only moderate compression, and the second part delves into the specific details of algorithms that have been designed for lossless image compression over the years. The chapter further elaborates the spatial and hierarchical prediction methods, error modeling, and scanning techniques. The adaptive predictors used in lossless image compression are classified as switched predictors or combined predictors. Detection-based predictors detect edges, gradients, or texture regions in images and combined predictors are also provided in the discussion along with its utilities and disadvantages in the application. There is also a section on performance-based blending of predictors and hierarchical lossless image coding.
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