Factors Affecting the Perception-Based Compression of Haptic Data

Abstract

The ability of technology to transmit multi-media content is very dependent on compression techniques since bandwidth affects how much information can be transmitted in a given amount of time. Researchers have investigated efficient lossy compression techniques for image compression (jpeg) (Miano J., 1999), audio compression (mp3) (Brandenburg K., 1999; Gersho A., 1994) and video compression (mpg) (Bhaskaran V., Konstantinides K., 1999) to facilitate the storage and transmission of audio and video. Recently, haptics is becomingmore important with its addition in various applications such as computer-aided design (CAD), tele-surgery, rehabilitation, robot-assisted surgery, and graphical user interfaces (GUI) to name a few. Haptic technology enables computer users to touch and/or manipulate virtual or remote objects in simulated environments or tele-operation systems. If haptic cues (e.g. touch sensations) are displayed in addition to visual and auditory cues, these VEs are called haptic-enabled virtual environments (HEVEs) (Srinivasan M. and Basgodan C., 1997). If haptic data is to be stored, transmitted and reproduced, the efficient use of the available bandwidth and computational resources is a concern. Most lossy audio and visual compression techniques rely on the lack of sensitivity in humans to pick up detailed information in certain scenarios. Similarly, haptic perception-based lossy compression techniques utilize limitations in the sensitivity of human touch to create haptic models with much less detail and thus requiring less bandwidth for a given sensation. Essentially, perception-based approaches use the threshold or just noticeable difference (JND) of force perception to develop efficient compression techniques. Force JND is the minimum difference that we can notice between two forces: the base force and an increment/decrement of the base force (Gescheider G.A., 1997). The haptic data would be stored or sent over the network when the value of sampled force data was greater than the force threshold value. It is thus necessary to quantify the force threshold and to investigate the impact of important factors on the force threshold. Most of the research in this field studied force perception with a human user in static interaction with a stationary rigid object (Hinterseer et al., 2005, 2006). It is equally important to 28