Seismic volume datasets are usually obtained in 32-bit floating point precision, which makes them difficult to be processed on commodity computer due to their large size and high dynamic range (HDR). In this paper, we present a novel quantization method which not only compresses the size of seismic datasets, but also preserves the important seismic features such as detailed structures, local relevance structures and singularities. Our method first identifies the sub range of detailed structures by measuring the difference between the histograms of original datasets and those smoothed by bilateral filter. Then, local relevance statistics (LRS) is proposed to determine sub ranges of local relevance structures. We also assign relatively small sub ranges for preserving some indispensable singularities. According to the piecewise coordinates, our feature-preserving quantization technique is conducted and the low dynamic range(LDR) datasets are generated. To further preserve the local contrast and keep the continuity of original datasets, tone reduction and B$\acute{e}$zier curve are resorted to optimize our quantization process. As will be shown in the comparative study, our quantified LDR datasets could be easily rendered on normal graphic hardwares, and the rendering results still present the features of interest, which facilitate further seismic interpretations.
['Junlian Shuai', 'Zhiguang Zhou', 'Bin Zhang', 'Gang Hua', 'Hai Lin']
Feature-Preserving Quantization for 3D Seismic Visualization