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Perception-Based Prediction for Efficient Kinesthetic Coding
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DOI：10.1109/LSP.2024.3426980.png)
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Abstract
En 中文
Integrating haptic feedback with audio and video not only expands the perceptual dimensions of multimedia applications but also enhances user engagement and experience. However, higher signal sampling rates and multi-degree-freedom in haptic interaction increase data significantly. For low-latency and reliable transmission of haptic signal (i.e. tactile and kinesthetic signals), efficient haptic coding is crucial. Existing algorithms overlook haptic signal characteristics, leaving room for improvement. We analyze the statistical characteristics of kinesthetic signals in-depth. Based on the local linear characteristics of position and velocity signals, and the sparse distribution of force signal, we propose an improved kinesthetic coding algorithm by combining dead-zone coding with segmented linear prediction. Extensive experiments on the standard datasets of the IEEE P1918.1.1 Haptic Codecs Task Group demonstrate the superior performance compared to state-of-the-art methods, achieving a more than halved reduction in data transmission rates with high signal-to-noise ratios and structural similarity.
Keywords:
Encoding
Haptic interfaces
Prediction algorithms
Force
Signal processing algorithms
Mathematical models
Linear regression
Data compression
haptics
kinesthetic coding
regression algorithm
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