How HEVC Video Upscaling Performance Compares Between Any Video Converter and AI-Based Solutions in 2024

I’ve been spending a good amount of time recently looking at how we take older video—specifically High Efficiency Video Coding, or HEVC, material—and make it look better on modern, high-resolution displays. It’s a surprisingly messy area. We all know that simply stretching a 1080p HEVC file to fit a 4K screen usually results in a blurry, artifact-ridden mess.

The question that keeps nagging at me is this: When we talk about “upscaling” in 2025, are we really getting better results from the established, traditional video conversion software, or have the newer, AI-driven methods actually crossed a performance threshold that makes them mandatory for serious quality improvements? I wanted to put these two approaches head-to-head, not based on marketing claims, but on what the output actually looks like when you scrutinize the details, particularly around complex textures and motion compensation.

Let’s first consider the traditional video converter approach. These programs typically rely on established interpolation algorithms, like bicubic or Lanczos resampling, sometimes with a bit of added sharpening applied post-scaling. They are fast, often hardware-accelerated using standard GPU pipelines, and they don't require massive amounts of VRAM or specialized machine learning frameworks to run. When you feed these tools a standard HEVC source, they perform a mathematical calculation on the existing pixels to generate new ones that fit the target resolution. This process is deterministic; give it the same input, and you get the same output every time. Where they often fall short, however, is when the original source material already has compression artifacts inherent to the HEVC encoding, like blockiness around high-contrast edges or mosquito noise in flat areas. The traditional scaler dutifully scales up those errors, sometimes even making them more visible due to the sharpening applied afterward. I find that while they are excellent for simple frame rate conversions or format changes, the actual perceptual quality improvement when pushing resolution boundaries is often minimal, bordering on negligible past a certain scaling factor, say 1.5x. They are essentially guessing based on adjacent pixel values, which is a fundamentally limited way to reconstruct missing detail.

Now, let’s turn the lens toward the AI-based solutions that have emerged over the last couple of years. These systems are trained on vast datasets of low-resolution/high-resolution image pairs, allowing them to learn what *should* be there, rather than just interpolating what *is* there. When processing an HEVC file, the AI model attempts to hallucinate plausible detail—reconstructing fine textures like fabric weave or individual strands of hair that were completely lost during the initial compression and downscaling process. This is where the performance divergence becomes stark. If the model has been trained well on similar content—say, high-bitrate film grain—it can produce results that look genuinely sharper and cleaner than anything a traditional algorithm can manage, effectively removing some of the pre-existing HEVC macroblocking artifacts in the process. However, this power comes with a significant trade-off: processing time and resource demands are substantially higher, often requiring dedicated Tensor cores or significant compute time even on powerful consumer hardware. Furthermore, there is the issue of fidelity versus hallucination; sometimes the AI invents detail that wasn't present and perhaps shouldn't be there, leading to an output that looks artificially smooth or overly detailed in ways that betray the original source material’s limitations. It becomes less about faithful reproduction and more about aesthetic interpretation, which is a critical distinction for technical evaluation.