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A large-scale video codec comparison of x264, x265 and libvpx for practical VOD applications
Author(s): Jan De Cock; Aditya Mavlankar; Anush Moorthy; Anne Aaron
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Paper Abstract

Over the last years, we have seen exciting improvements in video compression technology, due to the introduction of HEVC and royalty-free coding specifications such as VP9. The potential compression gains of HEVC over H.264/AVC have been demonstrated in different studies, and are usually based on the HM reference software. For VP9, substantial gains over H.264/AVC have been reported in some publications, whereas others reported less optimistic results. Differences in configurations between these publications make it more difficult to assess the true potential of VP9. Practical open-source encoder implementations such as x265 and libvpx (VP9) have matured, and are now showing high compression gains over x264. In this paper, we demonstrate the potential of these encoder imple- mentations, with settings optimized for non-real-time random access, as used in a video-on-demand encoding pipeline. We report results from a large-scale video codec comparison test, which includes x264, x265 and libvpx. A test set consisting of a variety of titles with varying spatio-temporal characteristics from our catalog is used, resulting in tens of millions of encoded frames, hence larger than test sets previously used in the literature. Re- sults are reported in terms of PSNR, SSIM, MS-SSIM, VIF and the recently introduced VMAF quality metric. BD-rate calculations show that using x265 and libvpx vs. x264 can lead to significant bitrate savings for the same quality. x265 outperforms libvpx in most cases, but the performance gap narrows (or even reverses) at the higher resolutions.

Paper Details

Date Published: 27 September 2016
PDF: 17 pages
Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997116 (27 September 2016); doi: 10.1117/12.2238495
Show Author Affiliations
Jan De Cock, Netflix, Inc. (United States)
Aditya Mavlankar, Netflix, Inc. (United States)
Anush Moorthy, Netflix, Inc. (United States)
Anne Aaron, Netflix, Inc. (United States)


Published in SPIE Proceedings Vol. 9971:
Applications of Digital Image Processing XXXIX
Andrew G. Tescher, Editor(s)

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