Vibrotactile Quality Assessment: Hybrid Metric Design Based on SNR and SSIM

Abstract

The emerging mulsemedia (MULtiple SEnsorial MEDIA) introduces new sensorial data (haptic, olfaction, gustation, etc.), significantly augmenting the conventional audio-visual communication. This can be used in many areas, such as immersive entertainment and innovative education. Previous research has been dedicated to evaluating the impact of other sensorial data on conventional multimedia; however, standalone quality evaluation of new sensorial data, especially vibrotactile data (a type of haptic data), has not been covered. To the best of our knowledge, this paper is the first to empirically demonstrate that the common statistical metrics in audio and visual domains, i.e. signal-to-noise ratio (SNR) and Structural SIMilarity (SSIM), are highly correlated with human vibrotactile perception as well. To be specific, we propose a testing protocol for vibrotactile quality evaluation and conduct subjective experiments. The results suggest that SNR and SSIM are applicable to vibrotactile quality assessment. We also consider a practical scenario where the quality of vibrotactile data varies with time. Based on the validation of SNR and SSIM in the first part, we present an objective metric as a hybrid composition of SNR and SSIM. Instead of assessing the quality of data using an overall score, the hybrid metric evaluates the quality in a time-varying manner. Subjective experiments are conducted and the results demonstrate that the correlation coefficient can be significantly increased using the hybrid metric.