Compression of Surface Texture Acceleration Signal Based on Spectrum Characteristics

• Published in: Virtual Reality & Intelligent Hardware Vol. 5; no. 2; pp. 110 - 123
• Main Authors: Dongyan Nie, Xiaoying Sun
• Format: Journal Article
• Language: English
• Published: KeAi Communications Co., Ltd 01.04.2023
• Subjects: Compression; Haptics; Spectrum characteristics; Surface texture; Vibrotactile
• ISSN: 2096-5796
• DOI: 10.1016/j.vrih.2022.01.006

Background: Adequate-data collection could enhance the realism of surface texture haptic online-rendering or offline-playback. A parallel challenge is how to reduce communication delays and improve storage space utilization. Methods: Based on the similarity of the short-term amplitude spectrumtrend, this paper proposes a frequency-domain compression method. A compression framework is designed, firstly to map the amplitude spectrum into a trend similarity grayscale image, compress it with the stillpicture-compression method, and then to adaptively encode the maximum amplitude and part of the initial phase of each time-window, achieving the final compression. Results: The 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 interact point. The subjective score for the similarity reached an excellent level, with an average score of 87.85. Conclusions: Our method can be used for offline compression of vibrotactile data. For the case of multi-interact points in space, the trend similarity grayscale image can be reused, and the compression ratio is further reduced.