Authors:
Ehsan Rahimi
and
Chris Joslin
Affiliation:
Carleton University, Canada
Keyword(s):
Stereoscopic Video, 3D/Multiview Video, Depth Map and Color Image, Multiple Description Coding, Error Prone Environment, Region of Interest, Pixel Variation, Coefficient of Variation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image and Video Coding and Compression
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Stereo Vision and Structure from Motion
;
Visual Attention and Image Saliency
Abstract:
In this paper, we introduce a new reliable method of stereoscopic Video Streaming based on multiple description coding strategy. The proposed multiple description coding generates 3D video descriptions considering interesting objects contained in its scene. To be able to find 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 3D video descriptions for the color video using a non-identical decimation method with respect to the identified objects. The objective test results verify the fact that the proposed method provides an improved performance than that provided by the polyphase subsampling multiple description coding and our previous work using pixel variation.