Statistically lossless image compression for CR and DR

This paper proposes an image compression algorithm that can improve the compression efficiency for digital projection radiographs over current lossless JPEG by utilizing a quantization companding function and a new lossless image compression standard called JPEG-LS. The companding and compression processes can also be augmented by a pre-processing step to first segment the foreground (collimation) portions of the image and then substitute the foreground pixel values with a uniform code value. The quantization companding function approach is based on a theory that relates the onset of distortion to changes in the second-order statistics in an image (i.e., the differences between nearest neighbor pixels). By choosing an appropriate companding function, the properties of the second-order statistics can be retained to within an insignificant error, and the companded image can then be lossless compressed using JPEG-LS; we call the reconstructed image statistically lossless. The approach offers a theoretical basis supporting the integrity of the compressed-reconstructed data relative to the original image, while providing a modest level of compression efficiency (approximate to 3X improvement over current lossless JPEG). This intermediate level of compression could help to increase the comfort level for radiologists that do not currently utilize lossy compression and may also have benefits from a medico-legal perspective.