Compression and smart coding of offset and gain maps for intraoral digital x-ray sensors

The response of indirect x-ray digital imaging sensors is often not homogenous on the entire surface area. In this case, calibration is needed to build offset and gain maps, which are used to correct the sensor output. The sensors of new generation are equipped with an on-board memory, which serves to store these maps. However, because of its limited dimension, the maps have to be compressed before saving them. This step is critical because of the extremely high compression rate required. The authors propose here a novel method to achieve such a high compression rate, without degrading the quality of the sensor output. It is based on quad tree decomposition, which performs an adaptive sampling of the offset and gain maps, matched with a RBF-based interpolation strategy. The method was tested on a typical intraoral radiographic sensor and compared with traditional compression techniques. Qualitative and quantitative results show that the method achieves a higher compression rate and produces images of superior quality. The method can be adopted also in different fields where a high compression rate is required.