Complexity-based consistent-quality encoding in the cloud

A cloud-based encoding pipeline which generates streams for video-on-demand distribution typically processes a wide diversity of content that exhibit varying signal characteristics. To produce the best quality video streams, the system needs to adapt the encoding to each piece of content, in an automated and scalable way. In this paper, we describe two algorithm optimizations for a distributed cloud-based encoding pipeline: (i) per-title complexity analysis for bitrate-resolution selection; and (ii) per-chunk bitrate control for consistent-quality encoding. These improvements result in a number of advantages over a simple “one-size-fits-all” encoding system, including more efficient bandwidth usage and more consistent video quality.

[1]  C.-C. Jay Kuo,et al.  Challenges in cloud based ingest and encoding for high quality streaming media , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[2]  Anil C. Kokaram,et al.  Multipass encoding for reducing pulsing artifacts in cloud based video transcoding , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[3]  Fan Zhang,et al.  Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments , 2011, IEEE Transactions on Multimedia.

[4]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Rahul Vanam,et al.  Improved Rate Control and Motion Estimation for H.264 Encoder , 2007, 2007 IEEE International Conference on Image Processing.