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2 months ago
For Your Eyes Only: Improving Netflix Video Quality With Neural Networks

Originally published in Netflix Technology Blog, Nov 14, 2022. 

When you are binge-watching the latest season of Stranger Things or Ozark, we strive to deliver the best possible video quality to your eyes. To do so, we continuously push the boundaries of streaming video quality and leverage the best video technologies. For example, we invest in next-generation, royalty-free codecs and sophisticated video encoding optimizations. Recently, we added another powerful tool to our arsenal: neural networks for video downscaling. In this tech blog, we describe how we improved Netflix video quality with neural networks, the challenges we faced and what lies ahead.

How Can Neural Networks Fit Into Netflix Video Encoding?

There are, roughly speaking, two steps to encode a video in our pipeline:

  1. Video preprocessing, which encompasses any transformation applied to the high-quality source video prior to encoding. Video downscaling is the most pertinent example herein, which tailors our encoding to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, multiple resolutions of a source video are produced. For example, a 4K source video will be downscaled to 1080p, 720p, 540p and so on. This is typically done by a conventional resampling filter, like Lanczos.
  2. Video encoding using a conventional video codec, like AV1. Encoding drastically reduces the amount of video data that needs to be streamed to your device, by leveraging spatial and temporal redundancies that exist in a video.

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