Bidirectional Texture Function Compression Based on Multi‐Level Vector Quantization

Abstract

Abstract The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real‐world material appearance. In this paper a novel BTF compression model is proposed. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multi‐dimensional conditional probability density functions. These functions are compressed in turn using a novel multi‐level vector quantization algorithm. The result of this algorithm is a set of index and scale code‐books for individual dimensions. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code‐books. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. The proposed model achieves low distortion and compression ratios 1:233–1:2040, depending on BTF sample variability. These results compare well with several other BTF compression methods with predefined compression ratios, usually smaller than 1:200. We carried out a psychophysical experiment comparing our method with LPCA method. BTF synthesis from the model was implemented on a standard GPU, yielded interactive framerates. The proposed method allows the fast importance sampling required by eye‐path tracing algorithms in image synthesis.