The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.
A @math -degree video provider (Facebook) has recently released detailed the implementation of its delivery platform @cite_1 . The spherical video is projected onto a pyramid layout from up to @math central points to generate a set of video representations. Since the front face of pyramid projection has a better image quality than the other faces, the system is in essence similar to our concept of QER . The end-users periodically select one of the representations based on their center. This implementation corroborates that, from an industrial perspective, the extra-cost of generating and storing multiple QER -based representations of the same video is compensated by bandwidth savings and enhanced system usability. However, as seen in , the pyramid projection is not the best regarding the viewport quality. Moreover, the system uses the same video quality on each face, which is less efficient than our proposal. Finally, the impact of the video encoding on the solution is not given.
@cite_1
2081773958 2111229404 2111085154 2146614659
In this paper we propose a novel method for human action recognition based on boosted key-frame selection and correlated pyramidal motion feature representations. Instead of using an unsupervised method to detect interest points, a Pyramidal Motion Feature (PMF), which combines optical flow with a biologically inspired feature, is extracted from each frame of a video sequence. The AdaBoost learning algorithm is then applied to select the most discriminative frames from a large feature pool. In this way, we obtain the top-ranked boosted frames of each video sequence as the key frames which carry the most representative motion information. Furthermore, we utilise the correlogram which focuses not only on probabilistic distributions within one frame but also on the temporal relationships of the action sequence. In the classification phase, a Support-Vector Machine (SVM) is adopted as the final classifier for human action recognition. To demonstrate generalizability, our method has been systematically tested on a variety of datasets and shown to be more effective and accurate for action recognition compared to the previous work. We obtain overall accuracies of: 95.5 , 93.7 , and 36.5 with our proposed method on the KTH, the multiview IXMAS and the challenging HMDB51 datasets, respectively. We present a novel client-driven multiview video streaming system that allows a user to watch 3D video interactively with significantly reduced bandwidth requirements by transmitting a small number of views selected according to his her head position. The user's head position is tracked and predicted into the future to select the views that best match the user's current viewing angle dynamically. Prediction of future head positions is needed so that views matching the predicted head positions can be prefetched in order to account for delays due to network transport and stream switching. The system allocates more bandwidth to the selected views in order to render the current viewing angle. Highly compressed, lower quality versions of some other views are also prefetched for concealment if the current user viewpoint differs from the predicted viewpoint. An objective measure based on the abruptness of the head movements and delays in the system is introduced to determine the number of additional lower quality views to be prefetched. The proposed system makes use of multiview coding (MVC) and scalable video coding (SVC) concepts together to obtain improved compression efficiency while providing flexibility in bandwidth allocation to the selected views. Rate-distortion performance of the proposed system is demonstrated under different experimental conditions. In existing pyramid-based spatially scalable coding schemes, such as H.264 MPEG-4 SVC (scalable video coding), video frame at a certain high-resolution layer is mainly predicted either from the same frame at the next lower resolution layer, or from the temporal neighboring frames within the same resolution layer. But these schemes fail to exploit both kinds of correlation simultaneously and therefore cannot remove the redundancies among resolution layers efficiently. This paper extends the idea of spatiotemporal subband transform and proposes a general in-scale motion compensation technique for pyramid-based spatially scalable video coding. Video frame at each high-resolution layer is partitioned into two parts in frequency. Prediction for the lowpass part is derived from the next lower resolution layer, whereas prediction for the highpass part is obtained from neighboring frames within the same resolution layer, to further utilize temporal correlation. In this way, both kinds of correlation are exploited simultaneously and the cross-resolution-layer redundancy can be highly removed. Furthermore, this paper also proposes a macroblock-based adaptive in-scale technique for hybrid spatial and SNR scalability. Experimental results show that the proposed techniques can significantly improve the spatial scalability performance of H.264 MPEG-4 SVC, especially when the bit-rate ratio of lower resolution bit stream to higher resolution bit stream is considerable. We present an algorithm designed for navigating around a performance that was filmed as a "casual" multi-view video collection: real-world footage captured on hand held cameras by a few audience members. The objective is to easily navigate in 3D, generating a video-based rendering (VBR) of a performance filmed with widely separated cameras. Casually filmed events are especially challenging because they yield footage with complicated backgrounds and camera motion. Such challenging conditions preclude the use of most algorithms that depend on correlation-based stereo or 3D shape-from-silhouettes. Our algorithm builds on the concepts developed for the exploration of photo-collections of empty scenes. Interactive performer-specific view-interpolation is now possible through innovations in interactive rendering and offline-matting relating to i) modeling the foreground subject as video-sprites on billboards, ii) modeling the background geometry with adaptive view-dependent textures, and iii) view interpolation that follows a performer. The billboards are embedded in a simple but realistic reconstruction of the environment. The reconstructed environment provides very effective visual cues for spatial navigation as the user transitions between viewpoints. The prototype is tested on footage from several challenging events, and demonstrates the editorial utility of the whole system and the particular value of our new inter-billboard optimization.