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With the growing demand for 3D video, efforts are underway to incorporate it in the next generation of broadcast and streaming applications and standards. 3D video is currently available in games, entertainment, education, security, and surveillance applications. A typical scenario for multiview 3D consists of several 3D video sequences captured simultaneously from the same scene with the help of multiple cameras from different positions and through different angles. Multiview video coding provides a compact representation of these multiple views by exploiting the large amount of inter-view statistical dependencies. One of the major challenges in this field is how to transmit the large amount of data of a multiview sequence over error prone channels to heterogeneous mobile devices with different bandwidth, resolution, and processing/battery power, while maintaining a high visual quality. Scalable Multiview 3D Video Coding (SMVC) is one of the methods to address this challenge; however, the evaluation of the overall visual quality of the resulting scaled-down video requires a new objective perceptual quality measure specifically designed for scalable multiview 3D video. Although several subjective and objective quality assessment methods have been proposed for multiview 3D sequences, no comparable attempt has been made for quality assessment of scalable multiview 3D video. In this article, we propose a new methodology to build suitable objective quality assessment metrics for different scalable modalities in multiview 3D video. Our proposed methodology considers the importance of each layer and its content as a quality of experience factor in the overall quality. Furthermore, in addition to the quality of each layer, the concept of disparity between layers (inter-layer disparity) and disparity between the units of each layer (intra-layer disparity) is considered as an effective feature to evaluate overall perceived quality more accurately. Simulation results indicate that by using this methodology, more efficient objective quality assessment metrics can be introduced for each multiview 3D video scalable modalities. ACM Reference Format:Roodaki, H., Hashemi, M. R., and Shirmohammadi, S. 2012 A new methodology to derive objective quality assessment metrics for scalable multiview 3D video coding. ACM Trans. Multimedia Comput.

With the increasing demand for 3D modeling by the emerging immersive applications, the 3D point cloud has become an essential representation format for processing 3D images and video. Because of the inherent sparsity in 3D data and the significant memory requirements for representing points, point cloud processing is a challenging task. In this paper, we propose a novel data structure for representing point clouds with a reduced memory requirement and a faster lookup than the state-of-the-art formats. The proposed format is examined for temporal encoding in geometric point cloud compression. Our simulation results show that the proposed temporal prediction enhances the compression rate and quality by 13-33% as compared to MPEG G-PCC. Moreover, the proposed data structure provides 16-54× faster point lookup operations and more than 1.4× reduction in memory consumption compared to the octree structure used in the MPEG G-PCC.

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The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Treebased structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based structures significantly suffer from irregular access patterns for large point clouds. Memory access indirection in such structures is disruptive to bandwidth efficiency and performance. In this paper, we propose a point cloud representation format based on compressed geometric arrays (CGA). Then, we examine new methods for point cloud processing based on CGA. The proposed format enables a higher bandwidth efficiency via eliminating memory access indirections (i.e., pointer chasing at the nodes of tree) thereby improving the efficiency of point cloud processing. Our experimental results show that using CGA for point cloud operations achieves 1328× speed up, 1321× better bandwidth utilization, and 54% reduction in the volume of transferred data as compared to the state-of-the-art tree-based format from point cloud library (PCL). Index Terms-point cloud representations point cloud operations spatial/temporal coding memory managementHoda Roodaki is with K.