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Publication Detail
Escaping the complexity-bitrate-quality barriers of video encoders via deep perceptual optimization
  • Publication Type:
    Conference
  • Authors:
    Chadha A, Anam R, Fadeev I, Giotsas V, Andreopoulos Y
  • Publication date:
    21/08/2020
  • Published proceedings:
    Proceedings of SPIE - The International Society for Optical Engineering
  • Volume:
    11510
  • ISBN-13:
    9781510638266
  • Status:
    Published
  • Name of conference:
    SPIE OPTICAL ENGINEERING + APPLICATIONS
  • Print ISSN:
    0277-786X
Abstract
© 2020 SPIE We extend the concept of learnable video precoding (rate-aware neural-network processing prior to encoding) to deep perceptual optimization (DPO). Our framework comprises a pixel-to-pixel convolutional neural network that is trained based on the virtualization of core encoding blocks (block transform, quantization, block-based prediction) and multiple loss functions representing rate, distortion and visual quality of the virtual encoder. We evaluate our proposal with AVC/H.264 and AV1 under per-clip rate-quality optimization. The results show that DPO offers, on average, 14.2% bitrate reduction over AVC/H.264 and 12.5% bitrate reduction over AV1. Our framework is shown to improve both distortion- and perception-oriented metrics in a consistent manner, exhibiting only 3% outliers, which correspond to content with peculiar characteristics. Thus, DPO is shown to offer complexity-bitrate-quality tradeoffs that go beyond what conventional video encoders can offer.
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