News broke (or “broke”) yesterday on the fact that AMD’s Fidelity FX Super Resolution (FSR) is derived from a Lanczos sharpening filter. The reason we put “broke” in quotes is because AMD literally published statements in its own publicly available source code acknowledging this fact:
The scalar uses a modified fast approximation to the standard lanczos(size=2) kernel. EASU runs in a single pass, so it applies a directionally and anisotropically adaptive radial lanczos. This is also kept as simple as possible to have minimum runtime.
The lanzcos filter has negative lobes, so by itself it will introduce ringing. To remove all ringing, the algorithm uses the nearest 2×2 input texels as a neighborhood, and limits output to the minimum and maximum of that neighborhood.
The only reason there’s even a whiff of controversy about this is because AMD told journalists that FSR represented a 100 percent in-house solution. There is a meaningful difference between “Developed 100 percent in-house” and “based on classic Lanczos scaling, but with various improvements.” AMD should have been straightforward to journalists about what it had created.
There is nothing intrinsically wrong with basing a sharpening approach on Lanczos filtering. Lanczos is a fairly common image upscaling algorithm that you’ll see used in applications like AviSynth. It’s commonly used because it works well. This doesn’t mean AMD didn’t modify it — the company made several changes to reduce ringing (over-sharpening artifacts) and to accelerate the application of the filter. It also open sourced its work so that developers could integrate it more easily.
As for whether or not this constitutes “upscaling,” it absolutely does. Upscaling existed long before artificial intelligence was formally applied to the problem and Lanczos filters are some of the best options for upscaling that were generally available. In the slider below I’ve upscaled two images using bicubic filtering and nearest neighbor filtering. That latter produces sharp edges and more noticeable pixelation:
https://ift.tt/2VtxP3i
AMD has positioned FSR as an “answer” to DLSS. Whether you think that’s a fair label or not probably depends on how you view the two technologies. At the highest level, both DLSS and FSR are intended to achieve the same goal, namely: Making your output look better without assessing a real penalty for doing so.
Nvidia created DLSS, an AI-based approach that leverages the tensor cores the company builds into its products. This capability is specific to Nvidia hardware. You can fairly argue that it represents a unique Nvidia value-add and that it will allow Nvidia GPUs to shift more resources towards ray tracing and away from rasterization. You can also argue that it represents yet another example of Nvidia deploying a technology intended to cater only to Nvidia users rather than doing anything for gaming and graphics as a whole.
AMD has built a solution that works on AMD and Nvidia GPUs from 2016 forwards, including Pascal, Polaris, Turing, Ampere, Vega, Radeon VII, RDNA, and RDNA2. It was never going to be the same kind of solution as DLSS because DLSS is Nvidia-tuned and Nvidia-specific. They are two different approaches to the problem of improving graphics quality without paying a heavy penalty for doing so.
Reviews of FSR generally suggest that the technology gives a moderate frame rate boost even in its highest “Ultra Quality” detail mode. This kind of feature could be particularly useful for integrated gamers wanting to improve 720p output to something more like 1080p without paying the corresponding resolution penalty. AMD could have — and should have — been more explicitly clear about what FSR was and what technology it was based on. Implementing a fast Lanczos filter with adaptations to prevent ringing is not nothing. The feature was capable of standing on its own two feet.
We suggest enabling features like FSR when they are available in any situation where you need to trade resolution for raw performance and are hoping to claw back a little image quality anyway.
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from ExtremeTechExtremeTech https://ift.tt/3Cp8NmE
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