There’s a lot going on in the world of video these days with the majority of footage having been created and distributed in high resolution formats for some time now.
However many people still have significant collections of older footage from back in the day that was made using analogue video tapes or digital DV sources.
Those formats were largely based on the standard definition television parameters of the time so on modern devices, can look a little tired!
Let’s face it, we have been recording digital and analogue video at home since the late seventies!
So that brings us to the subject of upscaling.
This is the process of increasing the resolution of a video to make it compatible with higher-resolution displays while at the same time at least maintaining or even improving image quality.
In this article I will get into into a little on the the history, current methods and future of video upscaling with a particular focus on how artificial intelligence (AI) is revolutionizing this field.
The History of Video Upscaling
Before we delve into the state-of-the-art AI upscaling techniques, it’s important to take a step back and examine the origins of video upscaling.
Early attempts at upscaling primarily relied on basic interpolation methods.
These techniques, such as nearest neighbor and linear interpolation focused on adding more pixels to an image to increase its size.
- Nearest Neighbor Interpolation works by selecting the nearest pixel value to assign to a new pixel in the upscaled image.
While it is computationally simple and fast, it often results in blocky images with a loss of detail.
- Linear Interpolation and Bicubic Interpolation are techniques that attempt to create new pixels by averaging the values of nearby pixels.
Although linear interpolation produces somewhat smoother images, it can still result in blurring and loss of sharpness which isn’t ideal for high-quality content.
Despite their ingenuity, these early methods were not particularly successful in producing videos that retained detail and clarity when scaled up.
This is primarily because they treated all parts of an image equally failing to account for the complexities and nuances of different scenes and subjects.
The Evolution of Upscaling: Introduction to AI and Machine Learning
With advancements in technology, the limitations of traditional upscaling methods paved the way for innovations powered by artificial intelligence and machine learning.
AI brings a paradigm shift by utilizing algorithms and models that can learn and predict details that are not explicitly present in the low-resolution input.
What is AI as Applied to Video?
AI, or artificial intelligence, refers to systems and algorithms that mimic human-like cognitive functions such as learning and problem-solving.
In the context of video upscaling, AI focuses on understanding patterns within visual data and intelligently enhancing resolution by generating plausible details.
A new era of video upscaling has been ushered in by AI techniques, specifically those using neural networks and deep learning.
These models can analyze countless frames and their high-resolution counterparts, learning to predict missing details more accurately.
To put it a little more simply the whole process goes a little like this:
The AI model is shown two images of exactly the same scene, one being a low resolution version and the other a high resolution version.
The model replicates those images internally (remember they are just a bunch of binary numbers to the AI) and then identifies what is different between the two.
This process is repeated millions of times until the AI model can accurately predict what each image consists of and what steps would need to be taken to take the low resolution image up to a higher resolution.
How AI-based Upscaling Works
Here’s a closer look at how these systems function:
Data Collection: The process begins with collecting data in the form of pairs of low-resolution and high-resolution images. This dataset serves as the training material for the AI model.
Training Phase: During training, the AI model – a neural network – learns the correlation between low-resolution images and their high-resolution versions.
It focuses on patterns such as edges, textures and contrast, gaining insights into how details can be reconstructed.
Application Phase: Once the model is trained, it can be applied to upscale real-world videos.
The AI system analyzes each frame, using its learned understanding to predict and fill in finer details, resulting in a crisper and more visually appealing image.
Throughout the testing and development phase there is considerable human feedback provided which serves to fine tune the learning process.
Several software applications now harness AI for video upscaling.
Tools like Topaz Video Enhance AI and features within a number of consumer level video editors exemplify how effective these advancements can be when incorporated into editing workflows.
Effectiveness of AI Upscaling
AI upscaling has proven to be remarkably effective compared to traditional methods, offering several key advantages:
- Enhanced Detail Retrieval: AI techniques significantly improve detail retention, making edges sharper and textures more defined, even in scenes with complex background patterns.
- Artifact Reduction: Unlike traditional methods that often introduce jagged edges and visual artifacts, AI models can seamlessly integrate details to create smooth transitions and natural appearances.
- Performance Variability: While AI upscaling requires substantial computing power, its results can vary based on the algorithm used and the inherent quality of the source video.
Current tools are optimized for speed and compatibility, ensuring that they cater to both amateur content creators and professional video editors.
Considerable improvements can be seen when AI is used for film restoration, where old footage is rejuvenated with impressive clarity and in enhancing legacy content for display on 4K or 8K screens without losing any critical visual information.
Pros and Cons of AI Video Upscaling
While AI has brought immense improvements to video upscaling, it’s essential to weigh its pros and cons:
Advantages:
- Superior Quality: AI’s learning capabilities allow for precise and context-aware enhancement, making videos appear as though they were originally captured in higher resolution.
- Continuous Improvement: As AI models train on more data over time, they become progressively better at predicting details, adapting to new types of content.
Limitations:
- Computing Requirements: The processing power needed for AI upscaling can be substantial, potentially necessitating investment in powerful hardware for optimal performance.
- Cost: Advanced AI tools often come with associated costs, particularly in the case of high-end software solutions designed for professional use.
Despite these challenges, the powerful impact of AI-based upscaling on video quality makes it an attractive option for anyone looking to enhance their media with cutting-edge technology.
Additional Resources
Although this is a field of technology that is rapidly evolving here are a few links to some software that is currently offering AI Video Upscaling.
Nero AI Video Upscaler
One promising alternative is the relatively new Nero AI Video Upscaler.
This is a true AI upscaler and from the results I have seen can work wonders with old footage.
You can see it for yourself here: Nero AI Video Upscaler
And here’s a little promo clip of the software in action. Bear in mind there is a free trial so that worth taking a look at first.
HitPaw VikPea (HitPaw Video Enhancer)
Originally called HitPaw Video Enhancer this one has had a name change to VikPea and I have no idea if that means anything to anyone but hey! Here we are!
This in another one I have downloaded and test for myself on some old DV .avi files from my original Sony DV cam.
Yes you read that right! A Sony DV cam with tapes and all that standard definition good stuff!
Anyway, it actually did a great job of taking that footage up to 1080p resolution as well as 4k and the improvement in image quality was excellent overall.
It also did a pretty good job as far as motion smoothing went considering it has to convert from interlaced footage to progressive.
You can check out a free trial here at: HitPaw VikPea Video Enhancer
And here’s a demonstration of it in action:
CyberLink PowerDirector
The CyberLink PowerDirector module has only recently been introduced to the 365 version and seems able to to provided quite exceptional results.
You can check it out for yourself here: CyberLink PowerDirector
Here’s a recent video showing their system in action.
Although there are a number of other alternatives none of the ones I have tested appear to be of great value considering the cost involved.
They also appear to to be limited in the amount of control you have over the process and to be honest I suspect they, or at least part of them, are not truly AI driven.
The “Rolls Royce” of upscalers presently and for a few years now is Topaz Labs “Video A.I” but coming in at around $300.00, that’s for you to decide!
The one thing I feel certain about at this moment is that in about six months to a year this topic will have progressed enormously.
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