Compression of surface texture acceleration signal based on spectrum characteristics

Abstract

BackgroundAdequate data collection can enhance the realism of online rendering or offline playback of haptic surface textures. A parallel challenge is to reduce communication delays and improve storage space utilization. MethodsBased on the similarity of the short-term amplitude spectrum trend, this study proposes a frequency-domain compression method. A compression framework is designed, which first maps the amplitude spectrum into grayscale images, compresses them with a still image compression method, and then adaptively encodes the maximum amplitude and part of the initial phase for each time window to achieve the final compression. ResultsThe comparison between the original signal and the recovered signal shows that when the time-frequency similarity is 90%, the average compression ratio of our method is 9.85% in the case of a single interaction point. The subjective score for similarity was found to be high, with an average of 87.85. ConclusionsOur method can be used for offline compression of vibrotactile data. For multi-interaction points in space, the trend similarity grayscale image can be reused, and the compression ratio is further reduced.