On the use of deep learning and parallelism techniques to signifcantly reduce the HEVC intra‑coding time

It is well-known that each new video coding standard signifcantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without afecting the rate/distortion (R/D) performance is therefore always a topic of intense research interest. In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to signifcantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel strategies based on frame partitioning is able to achieve speedups of up to 26× when 16 threads are used. The R/D penalty in terms of the BD-BR metric depends on the video content, the compression rate and the number of OpenMP threads, and was consistently between 0.35 and 10% for the video sequence test set used in our experiment

Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder

The proportion of video traffic on the internet is expected to reach 82% by 2022, mainly due to the increasing number of consumers and the emergence of new video formats with more demanding features (depth, resolution, multiview, 360, etc.). Efforts are therefore being made to constantly improve video compression standards to minimize the necessary bandwidth while retaining high video quality levels. In this context, the Joint Collaborative Team on Video Coding has been analyzing new video coding technologies to improve the compression efficiency with respect to the HEVC video coding standard. A software package known as the Joint Exploration Test Model has been proposed to implement and evaluate new video coding tools. In this work, we present parallel versions of the JEM encoder that are particularly suited for shared memory platforms, and can significantly reduce its huge computational complexity. The proposed parallel algorithms are shown to achieve high levels of parallel efficiency. In particular, in the All Intra coding mode, the best of our proposed parallel versions achieves an average efficiency value of 93.4%. They als

Optimizing the Transmission of Multimedia Content over Vehicular Networks

The multi channel operation mechanism of the IEEE 1609.4 protocol, used in vehicular networks, may impact network performance if applications do not care about its details. Packets delivered from the application layer to the MAC layer during a Control Channel time slot have to wait to be transmitted until the following Service Channel time slot arrives. The accumulation of packets at the beginning of this time slot may introduce additional delays and higher collision rates when packets are transmitted. In this work we propose a method, which we call SkipCCH, that deals with this issue in order to make a better use of the wireless channel and, as a consequence, increase the overall network performance. With our proposal, streaming video in vehicular networks will provide better reconstructed quality at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with QoS techniques, not only by increasing the received video quality, but also because it avoids starvation of the lower priority traffic