Impact of Machine Learning/AI On Video/Image Upscaling
Artificial intelligence (AI) works by using a mesh of simulated neurons. It enhances the computer’s ability to undertake jobs that routine programming can’t easily accomplish, such as prediction, interpretation and perception.
It is this unique ability of AI that makes it a perfect fit for incorporation into the media upscaling technology.
So, what does upscaling do? Upscaling adds extra pixels to a video or an image to make it viewable on a higher resolution display with minimal loss of quality. The AI algorithm responsible for upscaling usually fills in these extra pixels with values it deems most appropriate.
However, AI is only a recent introduction in the field of media upscaling. The classic methods used for upscaling ignored what AI deems extremely important—the context. Out of all the classic methods used for upscaling, interpolation is probably the most popular.
Although not very efficient, traditional upscaling methods worked because of the human brain’s inherent ability to draw logical inferences from any information it is fed with. It’s not difficult for a human being to perceive the object in a traditionally upscaled image or video, due to prior knowledge of the object’s characteristics stored in their brain. Human eyes are able to predict what the missing pixels values in a given visual content might be.
Artificial intelligence enabled upscaling is different. It optimizes images and videos using human-like intelligence. After being trained on large numbers of images with high and low resolution pairs, it attains the ability to scale similar images without difficulty. Hence, artificial intelligence has, in a way, redefined upscaling technology.
However, it is difficult to train an AI algorithm for this task. This is because it takes a lot of examples and a huge amount of computational power. Also, the trained model is able to perform well with images that are similar to the image pairs it was trained on, but not as well when dealing with images that are quite different. This means, when upscaling certain unique types of images or videos such as those from the retro times, a lot of care needs to be observed. You will either have to use a model that has been trained on content similar in nature to the case at hand, or pre-process the content to closely match the model’s low-resolution training inputs. Although training the model is an elaborate task and is difficult, running the model is not so much.
AI powered upscaling technology comes built in with many present-day devices such as TVs. Hence, such devices can dramatically upscale low-resolution content to fit perfectly on 4K, ultra-HD and 8K displays with minimal effort and time.
Note that ML is capable of enhancing visual content for a lot of industries including entertainment, surveillance and healthcare.
AI/Machine Learning at work—How does it upscale videos and images?
Machine learning makes use of neural networks for differentiating between high-resolution images and low-resolution ones. As discussed above, the neural network model undergoes training wherein it is exposed to millions of samples of low and high resolution content so that it learns from the inputs and delivers an output with a much higher quality compared to outputs obtained from non-AI upscaling methods.
Take, for instance, a piece of 1080p content. It is designed for displays with slightly more than two million pixels. However, 4K devices are almost 8.3 million pixels while 8K ones boast a staggering 33,000,000 pixels.
If you want to take the easiest route to fill in the additional pixels, you can opt for a process called doubling. In doubling, the visual information in existing pixels just gets repeated. This process is also referred to as nearest neighbour interpolation and is ideal for retro games.
This targets transitions in low resolution content. Unsightly transitions in images are caused by bright pixels positioned adjacent to dark pixels. So, what this algorithm does is it makes such transitioning appear smooth by using pixels having mid-level brightness at appropriate places. What results is a soft up-scaled image or video.
AI upscaling uses machine learning to determine what extra pixels should be displayed. AI uses information stored in its unique “database” in order to determine the best hue, saturation, and brightness for each individual pixel.
An upscaling algorithm uses cues from the input to determine what a particular footage would look like if it was shot in 4K and 8K.
An AI upscaler can not only sharpen borders of objects in images but also thin out the transition between dark and light. This can effectively prevent the halo effect caused by over-sharpening.
As with all types of image processing, balance is crucial. A lot of devices come built in with aggressive AI upscalers that provide the best balance between sharpness and detail and a natural-looking image.
To avoid any upscaler-generated detail fluttering around in moving images, the best upscalers look at the state and pixels across frames.
AI scaling is, at its core, a better way of upscaling media. It has improved in a very predictable fashion. It is an effective technique that, when done correctly, only enhances image quality and makes low-resolution sources look better on high-resolution displays.
Using machine learning to upscale content
Algorithmic advances have made it possible to improve the quality of visual content such as videos, images and CCTV footage. The machine learning model is able to fill in gaps in any pixelated image using its predictive power and guessing the exact pixel value in no time.
Machine learning can scale any low definition content for 4K devices in a seamless manner.
Machine learning powered upscaling can dramatically improve not just the quality but also the speed with which high resolution content can be delivered over television and OTT services.
How is AI powered upscaling different from traditional upscaling?
Today’s hardware can support both 4K and 8K content, but the practice of producing content to 8K quality standards is very recent. Here comes the role of AI upscaling. This technology is especially used to enhance the quality of visual content produced before the advent of 4K or 8K.
The traditional methods of upscaling (non-AI methods) were not capable of preserving sharpness and quality standards in converting a low-resolution content into high-resolution. Interpolation, a basic method that fills in missing pixels by either repeating pixela or using color values. This causes the image to get stretched. However, over-stretching of image pixels can cause blurry or muted results.
Since present-day TV displays and mobile screens support higher values of PPI or pixel-per inch ratios, conventional upscaling doesn’t prove very helpful. Such methods perform random stretching of visual content for 4k-8k and produce unsatisfactory results. Upscaling using machine learning automatically balances the pixels in accordance with the size of the display, contrary to traditional stretching methods. And that is why different manufacturers of desktops and televisions use machine learning to adjust the PPI ratio of images for their devices.
Let's discuss some applications of AI upscaling
Essentially, you will read about how businesses use this technology to improve customers’ experiences in the digital ecosystem.
- AI upscaling for high-quality visual content—
Video streaming businesses are striving for content perfection with a growing demand for a seamless viewing experience across various video consumption platforms. Machine learning in upscaling media content has made it possible for television broadcasting, as well as OTT platforms that enhance the viewer experience by providing high quality videos.
AI upscaling is not only for video content but also for a lot of other consumer-oriented services. Some of such use cases include optimizing product photos for e-commerce sites; enhancing the quality of imagery in the medical field, satellite image upscaling; improving online education by making it simpler for teachers to discuss complex subjects with high quality pictures and videos; and the like.
- Restoring old video games—
Contemporary video games usually feature high-quality visuals. However, professional gamers are embracing the trend of restoring old video games. There are dedicated software programs that use the machine learning technology for scaling up low resolution game visuals. And they are a big hit among people who are working heads down to restore old video games.
With AI upscaling, producing better-quality versions of classic games has become easy.
- AI upscaling for surveillance—
Another area of interest for AI scaling is video footage from drones, sensors and CCTV cameras.
The ability of AI to enhance visuals in real time are highly advantageous for surveillance companies, remote assessment and security checkpoints. This allows for the analysis of videos with high precision.
Machine learning is a huge opportunity for analysts to reduce the time spent on examining footages, and deriving actionable insights from them.
This may sound like normal upscaling. However, the AI part simply means that upscaling occurs with greater context awareness.
As we have learnt above, Al upscaling is a process that creates new pixels of information in a visual content to add detail to it. These new pixels fill in the gaps that form when a low resolution content is played on a high resolution display, and create a higher quality output. Machine learning algorithms are used in this process to improve the result.
So, AI/machine-learning is able to subject any visual content to the best resolution. However, this is only one of many benefits that AI upscaling can offer. This technology can also be used to reduce background noise and repair distortions caused by compression (used for reducing file sizes), and to categorize content based on video quality.
AI upscaling creates more natural-looking content by improving features like color, image depth, and line differentiation. These results make on-screen visuals look more real than ever.
Mogi’s Superior Transcoding Technique
Mogi I/O (www.mogiio.com) is an AI enabled Video & Image Delivery SaaS that helps Content Platforms to Improve Customer Engagement by enabling Buffer free Streaming Experience for the user through a patented multi-CDN upstream architecture called Mogi Streaming Engine, Enhanced experience through quality enhancement and compression of up to 50% both during transcoding itself and Deeper user insights through Advanced Video Analytics.
Mogi’s solutions are available end-to-end (Video Transcoding + Video Player + Mogi Streaming Engine (Multi-CDN delivery) + DRM + Video Analytics) or you can use individual products from the entire suite like just the Video Transcoding. Mogi also provides white-label end-to-end plug n play solutions for OTT and Edtech Platforms, with Web, Android and iOS apps as well as a dedicated CMS for OTT and LMS for EdTech.
Mogi uses AI to enhance the quality of images and videos. We have developed a TensorFlow model to enhance the quality, and have trained it with a huge sample of videos and images. We dynamically adjust the visual parameters of the input and enhance them per pixel in order to provide high-quality output. This is done while transcoding itself. One of the best individual products we have is our Transcoding Architecture, which is a unique cluster-based process, does the transcoding within 30% of the content length. The transcoding architecture’s result includes a highly compressed video of up to 50% with no loss in quality, and if you choose quality enhancement, a 40% compression with the enhanced video quality.
The pricing for Transcoding is very competitive as well, and along with it you get a highly compressed output with the same or higher quality. This means not only your contractual pricing is low due to competitive pricing, but your bandwidth consumption also reduces, and user experiences increase multifold. It’s a win-win for all of us (Users, Clients, Mogi).
If you want to partner with us and access our products, reach out to email@example.com