Analysis of compression techniques for stereoscopic images

Abstract

© 2018 St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences. All rights reserved. Virtual Reality (VR) and Augmented Reality (AR) Head-Mounted Displays (HMDs) have been emerging in the last years and they are gaining an increased popularity in many industries. HMDs are generally used in entertainment, social interaction, education, but their use for work is also increasing in domains such as medicine, modeling and simulation. Despite the recent release of many types of HMDs, two major problems are hindering their widespread adoption in the mainstream market: the extremely high costs and the user experience issues [1]. The illusion of a 3D display in HMDs is achieved with a technique called stereoscopy. Applications of stereoscopic imagining are such that data transfer rates and-in mobile applications-storage quickly become a bottleneck. Therefore, efficient image compression techniques are required. Standard image compression techniques are not suitable for stereoscopic images due to the discrete differences that occur between the compressed and uncompressed images. The issue is that the loss in lossy image compression may blur the minute differences between the left-eye and right-eye images that are crucial in establishing the illusion of 3D perception. However, in order to achieve more efficient coding, there are various coding techniques that can be adapted to stereoscopic images. Stereo image compression techniques that can be found in the literature utilize discrete Wavelet transformation and the morphological compression algorithm applied to the transform coefficients. This paper provides an overview and comparison of available techniques for the compression of stereoscopic images, as there is still no technique that is accepted as best for all criteria. We want to test the techniques with users who would actually be potential users of HMDs and therefore would be exposed to these techniques. Also, we focused our research on low-priced, consumer grade HMDs which should be available for larger population.