Psychohaptic Vibrotactile Encoding With Wavelet Compression

The patent transforms vibrotactile signals from the time domain to the frequency domain using Fourier transform, and then to the wavelet domain using discrete wavelet transform. This parameter transformation enables adaptive quantization based on human haptic perception characteristics, significantly reducing distortion while maintaining signal quality. The psychoacoustic model parameters are used to guide the quantization process, making it both simple to implement and highly effective.

The patent applies discrete wavelet transform to decompose the signal into different frequency subbands, then uses a psychoacoustic model to identify and prioritize important frequency components. By quantizing and encoding only the significant coefficients while discarding or coarsely encoding less important ones, the system achieves high compression ratios. The implementation remains straightforward as it builds upon familiar transform-based encoding frameworks.

The patent introduces a psychoacoustic model as an intermediary between the signal transformation and quantization stages. This model analyzes the frequency domain representation and generates quantization control signals that guide the encoding process to preserve perceptually important information. The intermediary layer adds minimal complexity while dramatically improving transmission accuracy by enabling adaptive, perception-based compression.

The patent applies local quality by treating different frequency subbands of the vibrotactile signal differently based on human haptic perception characteristics. The psychoacoustic model identifies which frequency regions are perceptually important and allocates more bits to encode them with higher precision, while using coarser quantization for less important regions. This localized, adaptive approach minimizes perceptual distortion while maintaining efficient compression.