The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Toward a psychophysically-based light reflection model for image synthesis
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
The Analysis and Recognition of Real-World Textures in Three Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
A data-driven reflectance model
ACM SIGGRAPH 2003 Papers
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Verification of rendering quality from measured BTFs
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Interactive editing and modeling of bidirectional texture functions
ACM SIGGRAPH 2007 papers
Visual equivalence: towards a new standard for image fidelity
ACM SIGGRAPH 2007 papers
The influence of shape on the perception of material reflectance
ACM SIGGRAPH 2007 papers
Extreme Compression and Modeling of Bidirectional Texture Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
On optimal resampling of view and illumination dependent textures
Proceedings of the 5th symposium on Applied perception in graphics and visualization
Interactive system for dynamic scene lighting using captured video environment maps
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
On uniform resampling and gaze analysis of bidirectional texture functions
ACM Transactions on Applied Perception (TAP)
A psychophysical evaluation of texture degradation descriptors
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
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Bidirectional Texture Functions (BTF) are commonly thought to provide the most realistic perceptual experience of materials from rendered images. The key to providing efficient compression of BTFs is the decision as to how much of the data should be preserved. We use psychophysical experiments to show that this decision depends critically upon the material concerned. Furthermore, we develop a BTF derived metric that enables us to automatically set a material's compression parameters in such a way as to provide users with a predefined perceptual quality. We investigate the correlation of three different BTF metrics with psychophysically derived data. Eight materials were presented to eleven naive observers who were asked to judge the perceived quality of BTF renderings as the amount of preserved data was varied. The metric showing the highest correlation with the thresholds set by the observers was the mean variance of individual BTF images. This metric was then used to automatically determine the material-specific compression parameters used in a vector quantisation scheme. The results were successfully validated in an experiment with six additional materials and eighteen observers. We show that using the psychophysically reduced BTF data significantly improves performance of a PCA-based compression method. On average, we were able to increase the compression ratios, and decrease processing times, by a factor of four without any differences being perceived.