Multiple image view synthesis for free viewpoint video applications

Interactive audio-visual (AV) applications such as free viewpoint video (FVV) aim to enable unrestricted spatio-temporal navigation within multiple camera environments. Current virtual viewpoint view synthesis solutions for FVV are either purely image-based implying large information redundancy; or involve reconstructing complex 3D models of the scene. In this paper we present a new multiple image view synthesis algorithm that only requires camera parameters and disparity maps. The multi-view synthesis (MVS) approach can be used in any multi-camera environment and is scalable as virtual views can be created given 1 to N of the available video inputs, providing a means to gracefully handle scenarios where camera inputs decrease or increase over time. The algorithm identifies and selects only the best quality surface areas from available reference images, thereby reducing perceptual errors in virtual view reconstruction. Experimental results are presented and verified using both objective (PSNR) and subjective comparisons

Segmentation-based Method of Increasing The Depth Maps Temporal Consistency

In this paper, a modification of the graph-based depth estimation is presented. The purpose of proposed modification is to increase the quality of estimated depth maps, reduce the time of the estimation, and increase the temporal consistency of depth maps. The modification is based on the image segmentation using superpixels, therefore in the first step of the proposed modification a segmentation of previous frames is used in the currently processed frame in order to reduce the overall time of the depth estimation. In the next step, a depth map from the previous frame is used in the depth map optimization as the initial values of a depth map estimated for the current frame. It results in the better representation of silhouettes of objects in depth maps and in the reduced computational complexity of the depth estimation process. In order to evaluate the performance of the proposed modification the authors performed the experiment for a set of multiview test sequences that varied in their content and an arrangement of cameras. The results of the experiments confirmed the increase of the depth maps quality — the quality of depth maps calculated with the proposed modification is higher than for the unmodified depth estimation method, apart from the number of the performed optimization cycles. Therefore, use of the proposed modification allows to estimate a depth of the better quality with almost 40% reduction of the estimation time. Moreover, the temporal consistency, measured through the reduction of the bitrate of encoded virtual views, was also considerably increased

3D video coding and transmission

The capture, transmission, and display of 3D content has gained a lot of attention in the last few years. 3D multimedia content is no longer con fined to cinema theatres but is being transmitted using stereoscopic video over satellite, shared on Blu-RayTMdisks, or sent over Internet technologies. Stereoscopic displays are needed at the receiving end and the viewer needs to wear special glasses to present the two versions of the video to the human vision system that then generates the 3D illusion. To be more e ffective and improve the immersive experience, more views are acquired from a larger number of cameras and presented on di fferent displays, such as autostereoscopic and light field displays. These multiple views, combined with depth data, also allow enhanced user experiences and new forms of interaction with the 3D content from virtual viewpoints. This type of audiovisual information is represented by a huge amount of data that needs to be compressed and transmitted over bandwidth-limited channels. Part of the COST Action IC1105 \3D Content Creation, Coding and Transmission over Future Media Networks" (3DConTourNet) focuses on this research challenge.peer-reviewe

Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is commonly synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this end, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will erase errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Experimental results show that the proposed scheme can outperform the use of a traditional feedback channel by up to 0.82 dB on average at 8% packet loss rate, and by as much as 3 dB for particular frames

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end