Bidirectional Texture Function Compression based on Multi-Level Vector Quantization
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
multidimensional 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.
Compression Algorithm Conceptual Scheme
Supplemental Materials:
(1) Research Report
V. Havran, J. Filip, K. Myszkowski: Bidirectional Texture Function Compression Based on Multi-Level Vector Quantization SUPPLEMENTAL
MATERIAL, Research Report 2265, Institute of Information Theory and
Automation, Academy of Sciences of the Czech Republic, 2009.
[PDF] [BibTex]
(2) Images and Movies - LDR and HDR materials
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