Perceptually optimal compression for heterogeneous image content in the context of medical networked applications

In medical networked applications, the server-generated application view, consisting of medical image content and synthetic text/GUI elements, must be compressed and transmitted to the client. To adapt to the local content characteristics, the application view is divided into rectangular patches, which are classified into content classes: medical image patches, synthetic image patches consisting of text on a uniform/natural/medical image background and synthetic image patches consisting of GUI elements on a uniform/natural/medical image background. Each patch is thereafter compressed using a technique yielding perceptually optimal performance for the identified content class. The goal of this paper is to identify this optimal technique, given a set of candidate schemes. For this purpose, a simulation framework is used which simulates different types of compression and measures the perceived differences between the compressed and original images, taking into account the display characteristics. In a first experiment, JPEG is used to code all patches and the optimal chroma subsampling and quantization parameters are derived for different content classes. The results show that 4:4:4 chroma subsampling is the best choice, regardless of the content type. Furthermore, frequency dependant quantization yields better compression performance than uniform quantization, except for content containing a significant number of very sharp edges. In a second experiment, each patch can be coded using JPEG, JPEG XR or JPEG 2000. On average, JPEG 2000 outperforms JPEG and JPEG XR for most medical images and for patches containing text. However, for histopathology or tissue patches and for patches containing GUI elements, classical JPEG compression outperforms the other two techniques.