Previously, I covered upscaling lower resolution video in my article, “How to Upscale Lower Resolution Video to 4K“.
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If you are using anything other than an Apple device, then the best graphics subsystem for the MPV media player today is Vulkan. This includes recent Windows-based PCs, Linux-based devices, and Android-based devices.
However, Apple devices running macOS, iOS, tvOS use Metal 2, not Vulkan. Currently, there is not a Metal backend for MPV. This means that MPV must use Apple’s deprecated OpenGL rendering subsystem. In the near future, it may be possible to use MoltenVK for a 2x performance gain over OpenGL. However, there is not an easy means (e.g. HomeBrew) to install MoltenVK on Mac, much less iOS and tvOS devices.
I maintain a Github repo with my recommended settings and user shaders, here. They are tailored to high quality rendering of traditional live TV and video disc formats. On Macs, you can download and install them in your ~[user profile]/.config/mpv folder.
Beyond upscaling, my configuration files include optimizations for resolution, colorspace, dithering, debanding, motion interpolation, and anti-ringing. And, FFMPEG’s bwdif filter for motion adaptive deinterlacing is applied to interlaced video, such as live TV.
Since I wrote the original version of this article, I have switched from RAVU prescalers to FSRCNNX for luma upscaling. For luma downscaling, I now recommend SSimDownscaler. For chroma upscaling and downscaling, I now recommend KrigBilateral.
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RAVU and NNEDI3 are neural network based upscalers. Both have trained weights by machine learning techniques. But, their model doesn’t have a deep architecture. RAVU is based on linear regression with a single layer. NNEDI3 has two layers with a single activation function.
By contrast, FSRCNNX, Waifu2X, and NGU use 2D convolution layers. Focusing on FSRCNNX: it is an implementation of the Fast Super-Resolution Convolutional Neural Network in TensorFlow, an open source machine learning platform.
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João Vitor Chrisóstomo recently profiled MPV’s luma and chroma upscaling algorithms, here. He compared performance in terms of frames per second. And, he evaluated quality based on SSIM, PSNR, MS-SSIM, PSNR-HVS-M and IW-SSIM measurements.
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For luma-upscaling on any content other than animation, he determined that FSRCNNX is the best quality upscaler, performance-wise. Due to how human eyes perceive chroma v. luma, the method of chroma-upscaling is less important. So, while KrigBilateral may be superior to other algorithms, if you are performance constrained on your computer or STB, you can fall back on Mitchell without a significant impact on video quality.
The profiles are defined in mpv.conf, above. Using the auto-profiles.lua script, MPV will apply different profile settings depending on the source video’s resolution, frame rate, and whether it is interlaced.
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Updated on March 23rd, 2019