Reliable 3D video streaming considering region of interest

• Main Authors: Ehsan Rahimi, Chris Joslin
• Format: Article
• Language: English
• Published: SpringerOpen 2018-06-01
• Series: EURASIP Journal on Image and Video Processing
• Subjects: 3D stereoscopic videos; Color video; Depth map image; Reliable multimedia streaming; Multiple description coding; Object identification
• Online Access: http://link.springer.com/article/10.1186/s13640-018-0273-y

Abstract 3D video applications are growing more common as communication technology becomes more predominant nowadays. With such increasing demand for the 3D multimedia services in either the wired or wireless networks, robust methods of video streaming will be introduced to show more favorable efficiency outcomes since packet failure is an integral characteristic of communication networks. This paper aims to introduce a new reliable method of stereoscopic video streaming based on multiple description coding (MDC) strategy. The proposed multiple description coding generates four 3D video descriptions considering the interesting objects contained in the scene. To be able to find the interesting objects in the scene, we use two metrics from the second-order statistics of the depth map image in a block-wise manner. Having detected the objects, the proposed multiple description coding algorithm generates the descriptions for the color video using a nonidentical decimation method with respect to the identified objects. To show how much reliable the proposed MDC method is, this article assumes that due to the unreliable communication channel, only one description, among four encoded descriptions, is delivered to the receiver successfully. Therefore, the receiver needs to estimate the missed descriptions’ data from the available description. Since the human eye is more sensitive to objects than it is to pixels, the proposed method provides a better visual performance in view of its subjective assessment. Although, the objective test results verify the fact that the proposed method provides an improved performance than the Polyphase SubSampling (PSS) multiple description coding and our previous work using pixel variation. Regarding the depth map image, the proposed method generates the multiple descriptions according to the pixel prediction difficulty level. The considerable improvement achieved by the proposed method is shown with the peak signal-to-noise ratio (PSNR) and Structural SIMilarity (SSIM) simulation result.