For years, advances in video stabilization technology have enabled increasingly higher degrees of stabilization. However, when watching video feeds using today’s most advanced video stabilization algorithms cranked up to maximum power, you’ll notice it doesn’t exactly look realistic. If you’re going to run machine learning algorithms on hours of video feed to identify patterns, then realism probably isn’t the most important thing. But if you’re going to share a video with fellow human beings, well, that’s another story. Let’s explore why this is the case and what it means for the video stabilization capabilities of tomorrow.
The role of AI in society at large in relation to video stabilization
As opposed to hardware-based video stabilization, such as gimbals and inherent chipset capabilities, software-based video stabilization using machine learning algorithms has become increasingly popular. Alongside this powerful use of AI in video stabilization, we are now increasingly seeing the emergence of AI in almost all aspects of society. For instance, the AI market is forecast to quadruple from 2020 to 2025, reaching a total value of USD 127 billion. To shed light on how today’s already powerful AI-based video stabilization algorithms can get better, let’s take a closer look at how AI is progressing in the rest of society.
AI-powered innovations are being used to improve performance and efficiency almost everywhere you look. But it’s not the rise of the robots. It’s about assisting humans and empowering them to focus on creative tasks. When you’re looking to obtain information quickly, today’s AI capabilities can save you time. For instance, you could use AI to automatically translate a website in another language or write an article for informational purposes. This would probably be the fastest way to find information – if there’s no need for any special experience.
Similarly, high-powered AI video stabilization can help you find information in a video feed quickly. If you’re going to train AI-powered bots on video feeds or written articles, then they would be happy to ingest data produced by fellow robots. But this might not leave the best impression if the goal is not strictly to gather information but rather to create an overall experience for humans. Accordingly, content intended to entertain, amaze, engage, captivate or induce some other kind of experience sets the bar higher.
As a result, ongoing development of AI algorithms for writing articles are focused on more closely approximating a professional human writer. Accordingly, next-gen AI-powered video stabilization algorithms stand to break new ground by getting better at mimicking a cameraperson. Let’s take a closer look at why we are drawn to video that appears to be a shot by a camera professional.
How humans perceive video quality
It’s hard to pinpoint exactly what makes for high video quality, as it can vary from person to person. However, perceptions of video quality are generally influenced by what we are frequently exposed to in society. Just as the fashion choices portrayed on television, in magazines, in stores and on the internet tend to leave their mark, the Hollywood movies and Netflix series we watch day in and day out set the standard for video quality. When we record video on a smartphone, action camera or drone to show friends or share in online communities, in many cases, we will unconsciously think it looks better if it conforms to what we’re used to.
Think about it: if the video is stiller than a Hollywood movie, it probably feels just as off and not quite right as if it’s shakier. And if panning is done in one super stable, robotic motion, wouldn’t that feel a little out of the ordinary? When all is said and done, you want the videos you record and watch to look like they were filmed by a professional cameraperson. This means we’re looking for a perfect, realistic balance, with video stabilization algorithms designed to mimic human movements.
Aligning video stabilization development and testing with the human perspective
Video stabilization quality has so far been largely tested, perceived and defined based on its sheer power and efficiency. To take into account the realistic, human perspective, we need to redefine how we perceive video stabilization quality. If we want to put the capability to create Hollywood-esque professional quality in the hands of everyday users, we need to set that as the standard for video stabilization development and testing.
For more insights on how tomorrow’s video stabilization can take into account the human perspective and how this will affect you, check out our guide, “Reimaging video stabilization quality”. Don’t hesitate to get in touch with us and engage in dialogue on your view of present and future video capabilities. For inspiration, insights and best practices for the next generation of video stabilization, enter your email address below and subscribe to our newsletter.
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