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Publication Detail
Toward Generalized Psychovisual Preprocessing For Video Encoding
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Chadha A, Anam MA, Treder M, Fadeev I, Andreopoulos Y
  • Publication date:
    10/05/2022
  • Pagination:
    39, 44
  • Journal:
    SMPTE Motion Imaging Journal
  • Volume:
    131
  • Issue:
    4
  • Status:
    Published
  • Print ISSN:
    1545-0279
Abstract
Deep perceptual preprocessing has recently emerged as a new way to enable further bitrate savings across several generations of video encoders without breaking standards or requiring any changes in client devices. In this article, we lay the foundation for a generalized psychovisual preprocessing framework for video encoding and describe one of its promising instantiations that is practically deployable for video-on-demand, live, gaming, and user-generated content (UGC). Results using state-of-the-art advanced video coding (AVC), high efficiency video coding (HEVC), and versatile video coding (VVC) encoders show that average bitrate [Bjontegaard delta-rate (BD-rate)] gains of 11%-17% are obtained over three state-of-the-art reference-based quality metrics [Netflix video multi-method assessment fusion (VMAF), structural similarity index (SSIM), and Apple advanced video quality tool (AVQT)], as well as the recently proposed nonreference International Telecommunication Union-Telecommunication?(ITU-T) P.1204 metric. The proposed framework on CPU is shown to be twice faster than × 264 medium-preset encoding. On GPU hardware, our approach achieves 714 frames/sec for 1080p video (below 2 ms/frame), thereby enabling its use in very-low-latency live video or game streaming applications.
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