Abstract
By 2022, we expect video traffic to reach 82% of the total internet traffic. Undoubtedly, the abundance of video-driven applications will likely lead internet video traffic percentage to a further increase in the near future, enabled by associate advances in video devices' capabilities. In response to this ever-growing demand, the Alliance for Open Media (AOM) and the Joint Video Experts Team (JVET) have demonstrated strong and renewed interest in developing new video codecs. In the fast-changing video codecs' landscape, there is thus, a genuine need to develop adaptive methods that can be universally applied to different codecs. In this study, we formulate video encoding as a multi-objective optimization process where video quality (as a function of VMAF and PSNR), bitrate demands, and encoding rate (in encoded frames per second) are jointly optimized, going beyond the standard video encoding approaches that focus on rate control targeting specific bandwidths. More specifically, we create a dense video encoding space (offline) and then employ regression to generate forward prediction models for each one of the afore-described optimization objectives, using only Pareto-optimal points. We demonstrate our adaptive video encoding approach that leverages the generated forward prediction models that qualify for real-time adaptation using different codecs (e.g., SVT-AV1 and x265) for a variety of video datasets and resolutions. To motivate our approach and establish the promise for future fast VVC encoders, we also perform a comparative performance evaluation using both subjective and objective metrics and report on bitrate savings among all possible pairs between VVC, SVT-AV1, x265, and VP9 codecs.
Highlights
Video streaming applications are steadily growing, driven by associate advances in video compression technologies
We compute forward prediction models to determine the mapping from the encoding parameters to the objectives of video quality, bitrate, and encoding time, in a live video streaming session, without having to encode the video
We present and discuss results using PSNR and Video Multi-Method Assessment Fusion (VMAF) for a variety of video resolutions
Summary
Video streaming applications are steadily growing, driven by associate advances in video compression technologies. Beyond that, synthesized video content augmented and virtual reality applications including 360o video content and point-cloud technologies [4]-[6], as well as internet gaming and emerging medical applications [7], [8] necessitate efficient solutions that will alleviate known bottlenecks, especially over resource constraint wireless networks Toward this direction, Versatile Video Coding (VVC)/ H.266 [9], the successor of the High Efficiency Video Coding (HEVC)/ H.265 standard [10], was officially released in July 2020 to reclaim the best compression efficiency available to the AV1 codec that was released in 2018 [11]. Both codecs target ultra-high-definition video coding, with a clear direction towards accommodating AR and VR applications, 360o and multi-view video coding
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