Can you really make old, low-res content look good on a 4K screen?
Short answer: Yes.
Long answer: Only if you do it right.
Why Upscaling Matters More Than Ever
Many operators are sitting on massive archives of lower-resolution content—from the SD-era or early HD, never intended for today’s ultra-high-definition screens. Play that back on a 4K screen and it shows its age. But remastering everything from scratch? Costly, slow, and often just not realistic.
Enter AI video upscaling.
Done right, it dramatically improves perceived video quality without touching the original source file. Think sharper edges, cleaner textures, fewer compression artifacts. It’s not a silver bullet, but in most cases, it gets your content looking good enough to meet today’s visual standards—and keeps your user experience consistent across devices.
What Can Go Wrong—and Why It Matters
Not all AI video upscaling is created equal.
Imagine a viewer launching a beloved TV classic, only to find faces distorted and backgrounds unnaturally warped. That’s not magic—it’s a mess. Poorly applied upscaling creates visual artifacts, distortions, and frame inconsistencies, breaking immersion and eroding viewer trust.
Bad upscaling doesn’t just look bad—it reflects poorly on your entire platform.
How AI Video Upscaling Actually Works
At a high level, video upscaling—aka video super-resolution (VSR)—means increasing video resolution so it looks sharp on higher-definition screens. Traditional methods rely on interpolation, often resulting in blurry output.
AI-based upscaling goes further. Deep learning models learn to reconstruct missing details from low-resolution inputs, predicting what should be there with remarkable accuracy.
The result? Sharper, more natural visuals that feel native to the display—without needing to remaster the source.
Key Challenges to Consider
While AI video upscaling holds promise, it’s not without challenges:
- Artifact Introduction and Temporal Inconsistency: Some models generate unnatural textures or flickering if not trained properly. Choose models that process sequences of frames, rather than individual ones, for smoother, more consistent results.
- Computational Demands: High-quality upscaling requires significant processing power, which can be a limiting factor for real-time applications. While GPU acceleration for AI model inference can significantly speed up the process, it is still more applicable for VOD processing rather than for live streaming.
- Increased bandwidth: Higher resolution often means higher bitrate. However, employing modern codecs with superior compression efficiency, such as HEVC, AV1, VP9, can reduce bitrates by more than 50% while maintaining the same visual quality. Make sure you use optimized encoding settings like variable bitrate (VBR) or multi-pass encoding to achieve maximum compression.
Deep Learning Models That Matter
Several advanced deep learning models are used in video super-resolution:
EDVR (Enhanced Deformable Video Restoration):
Use deformable convolutions to align and fuse features from multiple frames, capturing both spatial and temporal information for improved video restoration.
DUF (Dynamic Upsampling Filters):
A 3D convolutional network that excels at motion compensation and temporal consistency.
Non-Local Neural Networks:
These networks extract both spatial and temporal features by considering all possible positions as a weighted sum, potentially offering more effective results than local approaches.
Each has trade-offs, but applied correctly, they can deliver stunning results.
Training AI Models – Without Losing Your Mind
Training VSR (Video Super Resolution) models isn’t quick or easy. It involves two main steps:
Step 1: Data Collection
Acquiring high-quality datasets that accurately represent real-world video degradations is essential. However, collecting such data can be labor-intensive and may not cover the wide variety of degradations encountered in practice.
Use both synthetic and real-world datasets. Synthetic datasets can be generated by applying known degradation processes to high-quality videos, while real-world datasets can be curated from publicly available low-resolution videos. A mix of both can enhance the model’s ability to generalize across diverse scenarios.
Step 2: Model Training
Training a video super-resolution model can be a time-consuming process, often taking significantly longer than images. The duration depends on various factors including architecture complexity, dataset size, and hardware capabilities, which can take up to several days.
Optimize with strategies like:
- Multi-grid training: gradually increasing spatial and temporal sizes during training.
- Larger batch sizes and adaptive learning rates: speeds up convergence
- Code and hardware optimization: Use accelerated libraries and distributed training wherever possible.
Metrics That Actually Mean Something
In order to know how well a particular model performs, it is necessary to measure the quality of upscaled samples. Standard metrics like PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) may not fully capture perceptual quality, leading to discrepancies between quantitative scores and visual assessments.
That’s why perceptual metrics like VMAF (Video Multimethod Assessment Fusion), DLM (Deep Learning-based Metric), and ST-RRED (Spatio-Temporal Reduced References Entropic Differencing) are becoming industry standards. Even better? Run user studies to validate the subjective experience.
Bottom line: if your metric scores are high but your viewers are squinting, you’ve missed the mark.
Visual Results: Before and After AI Video Upscaling
Seeing is believing.
Below are real examples comparing original low-resolution frames with AI-enhanced results:
Take a close look at these frames—notice how much more detail is restored in facial features, textures, and backgrounds. This is what I look for when evaluating whether AI upscaling is truly doing its job.
When AI upscaling works, you see more than just pixels—you restore presence and clarity that reconnects the viewer with the content.
Original low-res image:
AI-upscaled high-quality image:
Original low-res image:
AI-upscaled high-quality image:
Business Value: Is AI Video Upscaling Worth It?
For most platforms, the answer is yes—and here’s why:
Extend Content Value: Instead of letting SD or early HD titles gather dust, you can upgrade them visually for modern screens. That means more hours of monetizable content—without reprocessing masters.
Improve Perception and Retention: Poor visual consistency silently drives churn. When you upscale video with AI, you standardize the user experience and signal that your platform values quality—no matter when the content was produced.
Operational Efficiency: It fits cleanly into VOD workflows allowing operators to process content offline without disrupting delivery. Paired with modern codecs and optimized encoding, the bandwidth impact is minimal.
It won’t fix every frame perfectly. But in most use cases, the quality leap is worth the effort.
What Operators Should Do Next
If you want to future-proof your library, AI upscaling isn’t just a tech upgrade—it’s a strategic move.
However, the key to success lies in selecting the right deep-learning models, validating results rigorously, and optimizing the full pipeline for performance and visual impact.
Focus on:
- Selecting the right VSR models for your content types
- Validating results visually, not just numerically
- Optimizing the pipeline from model to encoding to delivery
Done right, AI upscaling is not just a technical upgrade—it’s a content transformation strategy.
Curious About How to Implement AI Upscaling?
Setplex helps OTT providers integrate AI video upscaling into their content workflows, enabling platforms to revitalize libraries and exceed quality expectations.
Connect with Sergey on LinkedIn.
Book a demo of Setrix to see how Setplex simplifies VOD processing with AI.
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