Fast encoding for personalized views extracted from beyond high definition content

Broadcast providers are looking for new opportunities to increase user experience and user interaction on their content. Their main goal is to attract and preserve viewer attention to create a big and stable audience. This could be achieved with a second screen application that lets the users select their own viewpoint in an extremely high resolution video to direct their own first screen. By allowing the users to create their own personalized video stream, they become involved with the content creation itself. However, encoding a personalized view for each user is computationally complex. This paper describes a machine learning approach to speed up the encoding of each personal view. Simulation results of zoom, pan and tilt scenarios show bit rate increases between 2% and 9% for complexity reductions between 69% and 79% compared to full encoding.Broadcast providers are looking for new opportunities to increase user experience and user interaction on their content. Their main goal is to attract and preserve viewer attention to create a big and stable audience. This could be achieved with a second screen application that lets the users select their own viewpoint in an extremely high resolution video to direct their own first screen. By allowing the users to create their own personalized video stream, they become involved with the content creation itself. However, encoding a personalized view for each user is computationally complex. This paper describes a machine learning approach to speed up the encoding of each personal view. Simulation results of zoom, pan and tilt scenarios show bit rate increases between 2% and 9% for complexity reductions between 69% and 79% compared to full encoding.P