Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models
This work provides an efficient representation for haptic texture compression and generation, addressing the need for compact tactile signal models in digital touch applications.
The authors introduce two compact spectral envelope models (spectral beta and spectral slope) for representing tactile texture signals, achieving perceptual similarity comparable to high-fidelity recordings in a study with 14 participants. The spectral beta model, in particular, matched original reproductions in perceived realism.
Modern audio-visual media rely on compact representations for efficient storage and transmission, whereas realistic digital touch still depends on high-resolution tactile recordings. Existing approaches for representing tactile signals constrain manipulation and limit the generation of new content. Here, we introduce two compact representations, spectral beta and spectral slope, that capture the temporal spectral structure of finger-surface friction signals while preserving perceptually relevant information. Spectral beta models spectral skewness using a two-parameter beta distribution, whereas spectral slope approximates the spectrum with an asymmetric bandpass filter defined by low- and high-pass orders. We evaluated these representations in a perceptual study with 14 participants using five virtual textures rendered on a friction-modulation display and compared them with physical textures and high-fidelity reproductions of recorded signals. Spectral beta achieved perceptual similarity ratings comparable to those of the original high-fidelity reproductions. Regression analysis further showed that matching spectral energy across nine critical frequency bands was the strongest predictor of perceived realism. Together, these findings suggest that tactile texture perception depends primarily on fundamental temporal spectral patterns and that modeling these patterns is sufficient for perceptually realistic rendering. These results establish an efficient and scalable framework for haptic compression, communication, and synthetic texture generation.