We hear a lot in the media about Artificial Intelligence (AI) and Machine Learning (ML) and their big impact on our lives. Artificial Intelligence is the ability of machines to demonstrate intelligence. Machine learning is a subset of artificial intelligence, which has the ability to learn and improve without being programmed. With the big advances in cloud computing and cloud storage, machine learning has become a technology that can be accessible to everyone.
On the other hand, Artificial Intelligence and Machine Learning technologies have a big impact on the media itself, especially on video technology. In this article, we will see how AI technologies shape the video technology, and we’ll see how AI is used in video technology.
The Video, AI, and ML Connection
Video has an important role in any website or application. According to a forecast published by Cisco, by 2021 video traffic will be 82 percent of all consumer Internet traffic. The evolution and growth of video and image lead to the development of AI applications designed for various aspects of their usage over the internet.
The big variety and quantities of video content present a challenge on websites and applications on how to choose the best content for their users. This is where artificial intelligence can come to the rescue. Our online behavior and decisions are the data fed into AI algorithms. According to this information, AI decides which video it is going to recommend for us.
Another field where AI has a big impact is by providing tools for the video industry to efficiently and quickly edit their videos and images.
Use Cases for Machine Learning and AI in Video Technology
AI and machine learning have many use cases in digital video technology. Here are several of them:
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Video content recommendations ”subscription streaming services such as Netflix, Hulu, and Amazon Prime are using ML for generating customized intelligent content recommendations. These recommendations keep their customers consuming more content, and increase their revenues. Video-sharing platforms like YouTube also use AI to suggest their users what to watch next.
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Dynamic advertisements ”video sharing platforms and digital content creators are using ads for revenues. Machine learning algorithms can be used to learn from each user’s responses to ads and provide personalized ads. The ML can build a profile for each user, including viewer gender, geography, and demographics. The ML ability of continuous optimization over time will improve the profile accuracy and optimize the process of finding the most suitable ads for the user.
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Processing low-light images ”websites, publishers, and content creators wish to present high-quality images. Machine learning’s capabilities for processing images can correct images shot in low light. This can make more images suitable to be usable and presentable.
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Color-matching for video editing ”one of the challenges a video editor is facing is to match the colors of videos created by different types of devices. The editor task is to use raw clips which were taken by either smartphone, high-resolution cameras, and drones, and form a smooth video. AI-driven color-match video editing tools can be used to perform this task automatically.
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Video creation ”AI and ML technology can be used to generate videos based on a predefined phrase. Machine learning studies a large number of videos and learns to associate each one of them to a specific phrase. It can combine several video clips together to create a video.
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Video content search ”AI technologies can find which parts of a recorded video contain relevant content. It can be used for looking into security camera video for suspicious activity, or to create a library of a specific scene type.
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Sports analysis and reports ”AI and machine learning can be used for live sport events broadcasts. It can produce real-time match reports and immediate highlight clips during a match, or at the end of the match.
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Video compression ”AI also contributes to video compression. The target of video compression is to minimize bit rate while keeping picture quality. This is achieved by using content-aware encoding. This technology optimizes video encoding by detecting the areas in a picture where the human eye would focus. Along with video transcoding technology it brings to the user the best watching experience.
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Repair damaged images ”this AI capability was developed to repair damaged images. Damaged images are images that were erased or corrupted, usually as a result of human error. Such images can sometimes be partially recovered, which means that part of the image is missing or corrupted. AI can make an intelligent guess what the picture is missing, and restore it. This can be important in case the image has an important value. This feature can also be used to repair poor-quality, old black and white photos or hard-copy pictures that were damaged.
Wrap Up
Video and image technologies play an important part in our lives. In this article, we’ve seen how artificial intelligence and machine learning has become an essential technology to support video technology. It has a lot of influence on the way people create and consume images and videos. There is no doubt we will see more possibilities and a wider range of AI use cases in the field of video and image technologies.
