Dynamic bitrate allocation of interactive real-time streamed multi-view video with view-switch prediction

In multi-view video (MVV), the real-world scene is usually captured by more than two cameras positioned in an array. A viewer can consume MVV using either a non-interactive or an interactive transmission method. In the context of interactive MVV streaming, view switching may cause a long delay due to the frequent requests by the viewer. In this paper, we consider the use case of real-time interactive MVV (IMVV) streaming, where the view switching delay problem has a significant user experience impact. Our proposed method compress and send all the captured views using a dynamic bitrate allocation method. Also, a novel prediction algorithm has been used to choose possible views that the user might switch to. The predicted view switching is mapped to a hidden Markov model, and the transition probability is solved using Zipf distribution. The experimental results of the proposed method show a superior performance on an objective metric and view-switching delay for better viewing quality over the existing method.     

Adaptive multi-view video streaming using side information over peer-to-peer networks

Multimedia Tools and Applications - Multi-view plus-depth-map (MVD) video streaming with autostereoscopic displays provides multi-user immersive media experiences. In this context, delivery of MVD...     

Optimal image compression via block-based adaptive colour reduction with minimal contour effect

Current image acquisition devices require tremendous amounts of storage for saving the data returned. This paper overcomes the latter drawback through proposing a colour reduction technique which first subdivides the image into patches, and then makes use of fuzzy c-means and fuzzy-logic-based inference systems, in order to cluster and reduce the number of the unique colours present in each patch, iteratively. The colours available in each patch are quantised, and the emergence of false edges is checked for, by means of the Sobel edge detection algorithm, so as to minimise the contour effect. At the compression stage, a methodology taking advantage of block-based singular value decomposition and wavelet difference reduction is adopted. Considering 35000 sample images from various databases, the proposed method outperforms centre cut, moment-preserving threshold, inter-colour correlation, generic K-means and quantisation by dimensionality reduction.

     

A novel deep network architecture for reconstructing RGB facial images from thermal for face recognition

Multimedia Tools and Applications - This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face...     

Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions.