Mr. Ilya Fadeev Profile

Ilya Fadeev

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Proceedings Article | 21 August 2020 Presentation + Paper

Escaping the complexity-bitrate-quality barriers of video encoders via deep perceptual optimization

A. Chadha, R. Anam, I. Fadeev, V. Giotsas, Y. Andreopoulos

Proceedings Volume 11510, 115100C (2020) https://doi.org/10.1117/12.2567549

KEYWORDS: Video coding, Distortion, Human vision and color perception

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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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