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A Multi-Scale, Low-Parameter Rendering Algorithm for Virtual Textures

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As the technology enabling touch-sense rendering of virtual textures grows in efficacy and prevalence, so too grows the need for a standardized means of storing, transferring, and reconstituting textural signals. Furthermore, to achieve the ultra-low-latency requirements of the next generation of global communications networks, this digital texture representation must be data-efficient: retaining only the minimum set of textural features required to produce a rich virtual texture experience. In this thesis, I introduce the Texel rendering algorithm. Designed to exploit the limitations of human vibrotactile discrimination, this algorithm reproduces a rich and varied set of fine textures using only three parameters as input. Through the use of a series of psychophysical studies, I demonstrate that these parameters are sufficient to define self-similar fine textures; that close perceptual matches can be made using this algorithm to a diverse group of fine texture families; that the parameters can be successfully navigated even by users unfamiliar with virtual texture rendering devices, especially when enabled by a novel assistive algorithm; and that textures rendered with this algorithm are robust to additional compression via spectral quantization. Additionally, I describe the capacity of this algorithm to produce coarse textural features. In a psychophysical study, I demonstrate that the appearance of simultaneous fine textural features does not affect the perception of coarse features, suggesting that the algorithm is successful in leveraging the duplex theory of texture perception: the simultaneous and separate modes by which coarse and fine textures are perceived. In this way, the Texel rendering algorithm can be used as an effective tool to design perceptually-rich virtual textures requiring a very small amount of input data.

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