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- W2070904757 abstract "In medical networked applications, the server-generated application view, consisting of medical image content andsynthetic text/GUI elements, must be compressed and transmitted to the client. To adapt to the local contentcharacteristics, the application view is divided into rectangular patches, which are classified into content classes: medicalimage patches, synthetic image patches consisting of text on a uniform/natural/medical image background and syntheticimage patches consisting of GUI elements on a uniform/natural/medical image background. Each patch is thereaftercompressed using a technique yielding perceptually optimal performance for the identified content class. The goal of thispaper is to identify this optimal technique, given a set of candidate schemes. For this purpose, a simulation framework isused which simulates different types of compression and measures the perceived differences between the compressedand original images, taking into account the display characteristics. In a first experiment, JPEG is used to code allpatches and the optimal chroma subsampling and quantization parameters are derived for different content classes. Theresults show that 4:4:4 chroma subsampling is the best choice, regardless of the content type. Furthermore, frequencydependant quantization yields better compression performance than uniform quantization, except for content containing asignificant number of very sharp edges. In a second experiment, each patch can be coded using JPEG, JPEG XR or JPEG2000. 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 compressionoutperforms the other two techniques." @default.
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- W2070904757 date "2010-01-17" @default.
- W2070904757 modified "2023-09-23" @default.
- W2070904757 title "Perceptually optimal compression for heterogeneous image content in the context of medical networked applications" @default.
- W2070904757 doi "https://doi.org/10.1117/12.838722" @default.
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