AI-Based Scaling for Cost-efficient 4k UHD Content Delivery
Long gone are the days when content creation and delivery were dominantly manual processes. Today, a considerable chunk of the work is handled by machines, software programs and automated procedures.
The biggest force behind all this, without question, is AI or Artificial Intelligence. AI-enabled machines are smart and can considerably reduce human efforts in almost all imaginable fields. And they are capable of accomplishing many complex tasks that humans thought only they could do.
The impact of Artificial Intelligence on the scaling of visual content
AI works by using algorithms. To understand various AI technologies and the algorithms they use to perform tasks, profound technical know-how is required. And that is probably why it has always been difficult for new business players to tap AI’s potential fully.
Although cognitive technologies like machine learning, natural language processing, and robotics are not too easy to deal with, getting started with them has grown a hundredfold simpler over time.
Resultantly, we have more AI-enabled technologies now than we ever had. AI has made both processes and products smarter than ever.
AI also plays a major role in image and video scaling. AI-powered scaling is, in fact, way more efficient compared to traditional scaling methods.
AI scaling uses machine learning technology to improve a visual content’s perceived resolution. Regardless of whether you are using a high resolution or a low-resolution device, AI scaling optimizes the content on the display to offer you a fulfilling user experience. AI upscaling, for instance, can make even a 1K video content worth enjoying on a 4K device.
This is a delightful experience not just for viewers but also operators whose needs for remote caching, storage, and bandwidth get significantly reduced in comparison to delivering native 4K content. Hence, AI scaling may be considered a cost-effective solution for various stakeholders in the content marketing domain.
One thing that you need to bear in mind is that training AI algorithms are a rigorous process because of its compute-intensive nature. Most devices in use today come with in-built scaling technology that upscales or downscales input content to optimize it for seamless viewing. However, since this needs to be done in real-time without disturbing the viewing experience, the algorithms for scaling are meticulously chosen and the training they undergo is rigorous.
Delivering 4K content—What operators can do?
Operators who wish to provide 4K content must have a minimum of 2 compressed versions of it. One in 4K and the other in 1K or full high definition. Users having 4K resolution displays in addition to sufficient network bandwidth will receive the larger 4K file. Users having displays with poorer resolutions or bandwidth restrictions will receive the HD or FHD file. The system chooses appropriate segments from the 4K or 1K files at the core datacenter to satisfy adaptive bitrate control requirements. For viewers with 4K displays, this switching can cause annoying changes to the image quality.
Besides, 4K files can be quite large and duplicating program content will only result in excess remote cache space and storage being used up. Streaming 4K files also take up a considerable amount of bandwidth. This directly points at the fact that there is a limit on how many programs providers can offer in 4K at one time.
This picture is completely altered by AI-enabled super resolution. It makes it possible for operators to deliver a HD/FHD or 4K experience to viewers using a small program file of only 1K.
Scaling with CNN models—
It has been found with research that CNNs can reduce file size significantly by scaling each frame individually. This results in little to no loss of image quality on the end user’s device. Usually, two CNN models are created by content providers for all 4K contents that they wish to deliver. Each of these will be used as an input data for the CNN models. While one model is used to downscale each frame from UHD to FHD, the other to upscale the FHD frames into UHD. This rigorous training gives rise to a CNN model that has gained expertise in upscaling, and can restore textures, edge sharpness as well as minute details absent in the input 1K content.
Input specific training is imparted to the two models simultaneously. This means that the model assigned with the task of downscaling is now able to remove details in such a manner that the model meant for upscaling will be able to restore it correctly.
Let’s understand with an example. If you trace a picture and ask a skilled artist to make a real art out of the traced outlines, will they be able to do it? Well, without necessary instructions, the artist might fail. But by telling them about how you want the lines to be—jagged or smooth, by letting them know that the shape in the water is that of a dolphin, or giving out other relevant details, you will effectively “train” the artist, who will now be able to accurately fill in all the details that are not in the outline.
Machine-learning algorithms train in a similar fashion and scale visual content with great finesse. They give higher-definition outputs than what is achievable using compression methods that are strictly mathematical. Besides, ML scaling shows compatibility with standard video codecs.
The training technique used for the CNN models we discussed above is long and complex, just like any deep-learning network training, and is usually undertaken at a data center. But once trained, the models tasked with downscaling and upscaling jobs can show incredible performance speed.
The market is now home to powerful chip-level AI systems that can scale content on a real-time basis. In fact, the upscaling model can be small enough to operate even in a smart streaming device.
Delivering 4K content—What content creators can do?
Above, we have discussed 4K content delivery at the operator’s or broadcaster’s level, now we will see what content creators—both large and small scale— can do to make 4K content available to their users without spending a fortune.
Content creators can use AI enabled scaling tools readily available over the Internet in free as well as paid versions. AI upscaling is a game changing technology for the content market. It’s a highly efficient conversion technology that was introduced in 2008 by Samsung. AI makes use of machine learning technology to transform a low resolution visual content into a high resolution one. Although both 4K and 8K are a rage, 4K resolution is what most video playing devices like computers, smart phones and televisions feature.
The best thing about AI scaling is that it’s an intelligent technology and can enhance image and video quality in an incredible way. Starting from edge restoration and noise reduction to detail creation, you can do everything with an AI powered scaling tool for quality enhancement of your media content.
AI for scaling visual content
Talking about artificial intelligence, there are fundamentally two types of it. The first is referred to as general artificial intelligence or AGI. It is almost as intelligent as humans are. Robots are a great example of AGI. The second type is called narrow artificial intelligence or machine learning. Machine learning works by using neural networks which can be trained to recognize patterns in data sets. It is this specific type of artificial intelligence which is used by scaling tools to fill in missing pixels in images and video frames.
Although scaling visual content is something machine learning algorithms can handle in an efficient way, the training part can be a little complicated. Training involves feeding the algorithm at work millions of pairs of video clips or images in their high and low resolution versions, just so that it learns what low resolution looks like, and how it should appear once it is upscaled. This training is extremely important for the algorithm to learn how to scale visual content with minimum quality loss.
Once the algorithm has figured out how a low resolution visual content should appear at the desired output resolution, it starts filling in all the missing information in the input content with a human-like approach. As a result, realistic details get added to the output.
And that is why an AI upscaled 4K content is not much different from native 4K content.
AI scaling is a boon for content creators and providers alike. They can now eliminate the need to store or deliver native 4K content files without causing dissatisfaction among viewers. Upscaled 4K-quality content is highly enjoyable and doesn’t burn big holes in the pockets of stakeholders in the content market.
AI scaling already gives remarkable results, and one of the noteworthy characteristics of this technology is that it is only getting better with time.
We know that AI neural networks are able to be trained. If you have examples of inputs with “correct” outputs, you can expect the algorithm at work to produce correct answers. But that day is not too far, when separate training won’t anymore be required for specific tasks. Once trained to answer correctly based on inputs it has seen before, the algorithm might just be able to produce correct answers for inputs it has never seen.
Mogi’s Proprietary Video/Image Tech
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 Core Image Tech provides up to 80% lossless compression on images, making them extremely light for easy loading. It also auto-resizes images based on the screen size of the device to better optimize the image quality and the view . Finally, the smart crop also removes non-prominent areas from the images. Mogi’s Core Image Tech can be integrated seamlessly with your existing system. You can either choose your CDN or our CDN and we would work, either way, compressing and optimizing images on the fly. Clients have got up to 4x faster website load time, and 50% savings on bandwidth and CDN bills through lighter images.
Mogi’s Video Tech 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.
One of the best individual products we have is our Transcoding Architecture, which in 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 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 low due to competitive pricing, your bandwidth consumption reduces, and user experiences increase multifold. It’s a win win for all of us (Users, Clients, Mogi).
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