With changeset a41325fc854f, the x265 library can invoke the SVT-HEVC library for encoding through the —svt option. We have mapped presets and command-line options supported by the x265 application into the equivalent options of SVT-HEVC, and have added a few specific options that are available only when the SVT-HEVC library is invoked. This page in our documentation describes the steps to build, and invoke the SVT-HEVC library in more detail.

Our reason for this integration was to enable our users to evaluate additional relative trade-offs between performance and compression efficiency while working behind the familiar API of the x265 library. In the long term, we plan to leverage this integration to further improve x265’s ability to handle real-time and low turn-around scenarios in pure software; this is the space that SVT-HEVC was focused on. In parallel, we will continue to innovate on our flagship presets that are used in offline encoding where x265 dominates.  You can expect to see these changes in the coming releases of x265, increasing the reach of open-source for video compression!

We are happy to announce now have version 3.0 of x265. The main focus of this version is to improve the quality, especially for the ‘veryslow’ and ‘slower’ presets. Moreover, Dolbyvision is included in this version. The detail description of all the new features and releases is available in our release notes.

By Bhavna Hariharan

x265’s ability to leverage AI to accelerate encoding Adaptive Bitrate Streaming (ABR) has been presented in the paper titled “Adaptive Multi-Resolution Encoding Scheme for ABR Streaming” that is to appear in the proceedings of IEEE International Conference on Image Processing (ICIP), 2018. Adaptive streaming enables dynamic adaptation to changes in network conditions by encoding video content in multiple bitrates and resolutions. Multi-pass x265 encodes exploit the structural redundancy across multiple resolutions by sharing analysis data in the order of increasing resolution, thereby reducing the computational burden significantly.

Static and dynamic refinement features were conceptualized for the Adaptive streaming topology in x265. As the first step, the input video sequence is scaled down to the lowest resolution of the bitrate ladder and is encoded. Coding metadata such as CU quadtree structure, PU predictions, coding modes and motion vectors (MVs) are stored during this first pass encode at CTU level abstraction. The subsequent higher-resolution encodes invoke either the static or the dynamic refinement algorithm. The figure below depicts this architecture for a three pass system. Theoretically, this can be extended to N passes.

The static refinement algorithm may further be classified as intra-picture and inter-picture refinement wherein the information from the previous pass is used at different levels of granularity thereby giving the current encode a head start. The encoder intelligently refines the analysis data based on certain heuristics and later uses it in the Rate Distortion Optimization (RDO) process. By tweaking the degree of information that is being reused, the user can control the performance vs quality balance of the encode.

The dynamic refinement algorithm applies Bayesian classification on the information saved in the previous pass and identifies patterns between the complexity of the content and the refinement level being used. Based on these patterns, the dynamic refinement algorithm switches between the static refinement levels. This will allow the encoder to find the optimal balance between performance and quality while taking the content complexity into account, making it more efficient than the static refinement methods.

Compared to conventional single-resolution approaches, the computational complexity of higher resolution encodes is drastically reduced with the use of the AI-based adaptive multi-resolution technique. The adaptive multi-resolution scheme is able to achieve a whopping speedup of about 2.5X, with a negligible drop in the quality. The above table from the research paper compares the speed vs quality of static and dynamic refinement levels. Since the AI tools used in x265 are not based on CNN models, x265 is able to leverage the advances in CPU technology to achieve these improvements.

As it turns out, the x265 project turned 5 a couple of months back; our first commits from the HM encoder date back to March, 2013. And being the geeks that we are, we didn’t even realize it!

Many thanks to all those who have enabled x265 accomplish all that it has over 5 years! We continue to innovate inside x265 to improve both quailty, and performance and look forward to celebrating our 10th anniversary with you.

Cheers,

Team-x265.

By Pradeep Ramachandran

Now that the dust has settled, it is time to thank all the contributors for enabling a great showing by x265 at NAB 2018. We showed-off our new ML-accelerated content adaptive encoding for ABR, AVX-512 acceleration, and the recently added support for HDR10+/HLG at NAB. We received great feedback on what people would like to see in the coming releases, and will be working hard to continue to innovate in that space. We’ve also formed a committee to guide the future development of x265, and other open source media codecs that we will blog about more in the coming weeks; read this article for an initial idea of what this is about.

Until our next showing, don’t hesitate to reach out to us on the developer mailing list, doom9 (), or Facebook to talk.