Visually Lossless Compression of Dental Images

It has become a standard practice to use images in medicine. Due to increasing resolution, their size often occurs to be large and, then, necessity to compress them efficiently before storage and/or transmission arises. Since compression ratio of lossless compression is frequently limited, one has to apply lossy compression. Then, a problem of providing an acceptable quality to retain diagnostic value of compressed data appears. This paper deals with analyzing opportunities to perform this in non- iterative way for dental medical images for two versions of a coder based on discrete cosine transform (DCT) – AGU and AGU-M. It is demonstrated that mean squared error (MSE) and MSE modified with taking into account peculiarities of human vision system (MSEHVS) of distortions due to lossy compression can be predicted before starting compression itself. Then, a desired quantization step (QS) for AGU or scaling factor (SF) for AGU-M can be adjusted to provide a desired quality. Regression uses statistics of alternating current (AC) DCT coefficients calculated in 300…500 8x8 pixel blocks to predict output metrics using fitting curves in preliminary obtained scatter-plots.