Deciphered: AMD FSR vs NVIDIA DLSS

In the rapidly evolving landscape of gaming, graphics quality is a critical factor for both gamers and developers. To meet the demand for enhanced visuals without sacrificing performance, leading companies have introduced technologies that aim to improve graphics quality: AMD’s FidelityFX Super Resolution 2.0 (FSR) and Nvidia’s Deep Learning Super Sampling (DLSS). These techniques utilize different approaches, and understanding the philosophy behind them can shed light on their differences.

As hardware, especially GPUs, continues to advance, the graphics quality in games also improves. However, the advancements in rendering technologies and GPUs don’t always align seamlessly. The rise of ray tracing in gaming, made possible by Nvidia’s Turing GPUs, has ushered in a new era of visually stunning, mathematically accurate lighting. Real-time ray tracing brings lifelike lighting to interactive environments, but it also sparked the interest in image upscaling and reconstruction techniques.

The fundamental idea behind these techniques is to render graphics at a lower resolution, perform all the necessary calculations and interactions, and then upscale the image to the desired resolution. This concept forms the basis for both FSR and DLSS, albeit with different implementations.

AMD FidelityFX Super Resolution (FSR) is available in two versions: FSR 1.0 and FSR 2.x. FSR 1.0 is a spatial upscaler that operates late in the rendering pipeline, after anti-aliasing. It upscales a low-resolution frame to the target resolution and employs a two-pass process involving up-sampling and sharpening. While FSR 1.0 lacks some of the benefits and performance improvements of its successor, it remains usable on a range of GPUs.

FSR 2.x, including the latest iteration FSR 2.2, takes a temporal approach and replaces the game’s built-in Temporal Anti-Aliasing. It uses more data points from the frame, depth buffer, motion vectors, and color buffer, resulting in a superior image quality. FSR 2.0 also operates with smaller input frames compared to FSR 1.0, yielding improved performance. This version has gained widespread adoption and can be found in most games today.

Nvidia’s DLSS 1.0, on the other hand, is a spatial image upscaling technique that relies on neural networks. It comprises two stages: image enhancement and upscaling. The image enhancement network utilizes the current frame and motion vectors to enhance edges and reduce aliasing. The upscaling process takes a single low-resolution frame and uses neural networks trained on Nvidia’s supercomputers to expand it to the desired output resolution. However, the initial version of DLSS had some drawbacks, including a relatively soft image and artifacts due to the limited input data.

In conclusion, both AMD FSR and Nvidia DLSS aim to enhance graphics quality while maintaining optimal performance. However, they employ different techniques and have evolved over time to address their shortcomings. Understanding the underlying philosophy and differences between these technologies can help gamers and developers make informed decisions about their graphics solutions.


1. Which AMD GPUs support FidelityFX Super Resolution (FSR)?

FSR is supported by a range of AMD GPUs, including the Radeon RX 6000 Series, Radeon RX 5000 Series, Radeon RX 500 Series, and some older AMD Radeon GPUs.

2. Do I need specific hardware to use FSR or DLSS?

Both FSR and DLSS do not require dedicated hardware units. They can be utilized on a variety of modern GPUs from multiple vendors.

3. How does FSR 2.0 differ from FSR 1.0?

FSR 2.0 introduces a temporal approach, replacing the game’s Temporal Anti-Aliasing. It uses more data points and operates with smaller input frames, resulting in improved image quality and performance compared to FSR 1.0.

4. What were the limitations of Nvidia DLSS 1.0?

The initial version of DLSS had some drawbacks, including a relatively soft image and artifacts. These issues were attributed to the limited scope of data, as it relied on a single frame for input.


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