Technologies like deep learning, machine learning, and natural language processing are the next transformational wave for video production, development, and broadcasting.

Blistering progress, unparalleled success, and unmatched viability are just a few phrases associated with the infusion of AI across various industrial sectors. Artificial intelligence (AI) is at an important transition point in a world where technological advancements are at their finest.

The rate of growth of AI is extremely high such that the global artificial intelligence (AI) market size, valued at $27.23 billion in 2019, is projected to reach $266.92 billion by 2027, exhibiting a CAGR of 33.2 percent during the forecast period.

The upcoming boom in the video industry integrated with AI has shown immense progress. Technologies like deep learning, machine learning, and natural language processing are the next transformational wave for video production, development, and broadcasting.

Content that is truly “magnificent” can be time-consuming to create and often very expensive to produce. As it becomes costlier to produce content, it also becomes even more imperative to command the interest and attention of the audiences. AI augments the video asset pool to ensure that every type of audience is catered to and engaged across platforms.

How AI works?

AI can recognise individual content, generate autonomous clips, optimise video playback, and align advertisements and content based on viewer interests.

Some of its features are:

• Visual recognition
• Deep video analysis
• Translation
• Transcription and tagging
• Format adaption

These features are transforming our understanding of video content and as the AI learns more it evolves its models to have a more nuanced understanding of the elements of any content, making it much easier for teams to find the right content and make informed decisions on their utility, all backed by data.

Why is AI the future of video production?

AI has played a crucial role in bringing innovative and more efficient solutions to the video industry. It not only helps bring precision in the video content production but also enables a clear understanding of what interests the audience the most and delivers it as quickly as possible.

AI platforms can prove beneficial across various industries such as sports, media, entertainment, and many more that incorporate video as their primary content.

On an average, people watch almost eight hours of video content online in a week, which is growing by 16 percent YoY, fuelled by video consumption on social media platforms that receive 48 percent more views on an average than on other platforms.

In the age of explosion of content, smart solutions are needed to keep audiences engaged, and AI is at the forefront of it.

Using AI to create bite-sized-videos such as teasers, highlights, and web stories has helped content creators explore social channels and improve engagement significantly.

In addition, AI also streamlines post-production processes by reducing the time earlier spent on manually adding graphics, logos, and overlays on videos, ultimately leading to the creation of engaging content for the viewers increasing the speed to market. Interesting, isn’t it? How merely incorporation of a platform can do wonders.

AI makes it relatively easy for organisations to produce new video content quicker and lets them focus on more important matters such as producing more quality content rather than spending that important time on time-intensive manual analysis of videos.

AI also moderates the risk of launching new products/technologies on the market. For instance, IBM produced a horror movie trailer called Morgan using artificial intelligence. To cull out the list of spectacular moments for the trailer, the research team trained the AI system on scenes from 100 horror movies, which were analysed based on their visual and audio effects.

The trailer released in 2016 took 24 hours to create after Watson provided the filmmaker 6 minutes of carefully selected footage. Traditionally, this exercise would have taken anywhere between 10-30 days.

Conclusion

AI and ML have their own uncharted terrain and challenges, but they are positioned for greater objectives with incomparable proficiency. Artificial intelligence will continue to be a major technological asset for growing the popularity of video services as it necessitates using more effective and reliable video quality measurement and analysis techniques and charts a more interactive experience for the end-user.

Broadcaster, OTT platforms, content creators, and social media platforms are at the precipice of a great age of communication, which is going to be led by video content, and to make sense of this inevitable explosion of content, new-age technologies will need to be embraced, none more important than artificial intelligence.

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After a long day at work, you are dazed but at the same time determined to watch the newest episodes of your favourite series. And just when you’re in the nail-biting scene, the video has paused, then begins to buffer and never resumes! After a few frustrating seconds later, the video is at its lowest resolution, hardly showing clarity.

How did an exhilarating affair end into an arduous one?

Say Hello, to Video Streaming challenges. Honestly, This is not how a video should be streaming. That is when the potential game-changers like AI and Deep Learning comes into action. They can ensure Zero-delay, High Definition, enhanced streaming OTT making the user experience more enjoyable and worthy.

Adaptive BitRate

OTT or Over the Top content makers, as well as the providers, are launching intriguing content every single week. But the Quality of experience which is generally referred to as QoE is one main issue that’s not been able to tackle well enough by the OTT platforms.

QoE is about a video’s overall resolution, startup time, stalls and pauses that happen through the relay. Seamless delivery of video is highly reliable on internet speed or the so-called bandwidth which can drastically affect the user experience. That is when this ABR comes into play tackling the challenges with an innovative algorithm.

The parameters of a video have been “adapted” dynamically by the performance of your network. Therefore, compromising the quality of the video. But, it is still dependent on internet speed. It is 2021, and a lot of talks is happening around Deep Learning.

Intelligent Solutions

Videos have been the new type of data communication, literally an extension to an email or text. This has created a necessity for developments in video streaming with optimization of network and data. Most of the happening these days are unperceptive to the content it holds. The bitrate of a scene where the characters are just having a conversation differs from that of an action scene. Imagine seeing the wrinkles on Dumbledore’s face when he raises his wand, but unable to see clearly when Harry is fighting Voldemort.

And that needs an answer to the following questions

• What if a scene’s content could be comprehended?
• What if streaming algorithms learn the QoE by every scene?
• What if the encoding is instructed with relative importance to all frames?
• What if the streaming is friendly on any device?

The best possible answer is Deep Learning.

Deep Learning

Artificial Intelligence and Deep Learning are advancing technologies to improve video content and user’s QoE. Content conscious AI can remarkably improve the viewing experience making it personalized, immersive and novel. DNN (Deep Neutral Network) can be made device-aware, dynamically computing it with the strategic resources to scale up the performance of streaming.

An intelligent and notable method to enhance QoE is when the encoder looks at the entire video than looking at the pixels in individual frames. This helps for

• A better understanding of content
• Delivering the highest quality stream
• Identifying the content redundancies.

How can the impact of Deep Learning take place?

Device Aware

The device quality plays a major role in the entire process of decoding. Making it device friendly by all means, says that people can watch it anytime, anywhere and by any device. A click on the app of their smartphones. Because smartphones are the new laptops. Having the track on which device is targeted by the user can make his/her personal experience most engaging. The unique potential of a device can be utilized as its soul ability to redefine the entire streaming.

Context-Aware 

It doesn’t end there at being device-aware. Deep Learning and Artificial Intelligence can be made to pay specific attention to the entire context of the video, search preferences, genre interest. For instance, a user is streaming a particular series every week. Deep learning can use this data to show the user the preference show, the similar genres, other shows from the director and cast. This not only improves the QoE of the user but also makes it a Win-Win for the OTT providers as well.

Understanding the business of Streaming  

One might wonder why an OTT platform has to invest in such user engaging technologies? The answer to this is a recent survey done across 1000 customers who are using the OTT streaming in Urban India. As many as 62% of people viewing through the OTT face issues like buffering, pausing and hang of the app whilst used on travelling.

More than ever, In recent times, the customers are making it clear by immediate feedbacks on official and social media sites regarding any dissatisfaction While using the OTT. This shows a clear intolerance in regards to lower QoE which can affect the revenue. Long load times, buffering, pixelation and stalled videos should no longer be seen to sustain this business.

The emerging trends, the pandemic situation has given new dynamics to online streaming. The limitations to short films or fiction videos have widened up to live stream of sports, gaming and releasing movies. Thereby generating more revenue.

Gone were the days of gaming with Personal Computers. But, games like PUBG having pulled greater crowds just from a smartphone. The new gaming platform from Google, stadia gave an announcement stating as low bandwidth as 25mbps is enough for exceptional streaming.

This tells us how cost-effective ideas are emerging to change the phase of this business using AI and deep learning. A recent survey showed that 53.98% of viewers were willing to upgrade to premium subscriptions for intriguing content that has minimal streaming challenges.

Finally, The big picture of delivering high-quality videos at lower cost subscriptions to generate greater revenues is in view. The considerable benefits obtained from the usage of AI and the impact of deep learning in creating a greater streaming experience has solved many practical conditions. The biggest innovations of all this process are DNN based advancements. So, very soon this is going to change all the demographics to matchless QoE and incredible video consumption.

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Recent enhancements in video analytics with deep learning have been offering a distinct advantage

We are facing a daily reality such that we are encircled by so numerous intelligent video-capturing devices. These devices catch information about how we live and what we do. For instance, on account of surveillance and action cameras, as well as smartphones and even good old camcorders, we can record videos at an uncommon scale and speed. There is really rich data and information implanted in each one of those recordings.

Recent enhancements in video analytics have been offering a distinct advantage, ranging from applications that count individuals at occasions, to automatic license plate recognition, alongside facial recognition or smart parking.

All credits go to the colossal advances made in deep learning, video analytics has presented the automation of tasks that were at one time the selective domain of people. Today, video content analysis programming has developed to process the video and recognize, distinguish and characterize the items that show up and deliver accessible and filterable video information that can drive broad analytic capabilities.

Machine learning and, specifically, the fabulous improvement of deep learning solutions, has changed video analytics.

The utilization of Deep Neural Networks (DNNs) has made it conceivable to prepare video analysis systems that impersonate human conduct, bringing about a change in outlook. It began with frameworks dependent on exemplary computer vision methods (for example, setting off an alert if the camera picture gets excessively dim or changes radically) and moved to systems equipped for distinguishing explicit items in a picture and tracking their path.

For instance, Optical Character Recognition (OCR) has been utilized for quite a long time to remove text from pictures. On a fundamental level, it could do the trick to apply OCR algorithms straightforwardly to a picture of a license plate to perceive its number. In the past worldview, this may work if the camera was situated so that, at the time of executing the OCR, we were sure that we were recording a license plate.

Deep Learning is basically a training convention by which a machine is presented to volumes of labeled data to “learn” to perceive and distinguish similar data in new data sets. Copying the manner in which a human is educated, deep learning empowers technologies to all the more capably distinguish and recognize objects based on increased exposure to information. Driven by strong hardware infrastructure, deep learning empowers quicker analytic output, increased object detection, improved processing performance, recognition accuracy.

Because of the noteworthy triumphs of deep learning applications, we are currently ready to support video analysis performance significantly and start new studies to analyze video content. For instance, convolutional neural networks have exhibited prevalence on demonstrating significant level visual concepts, while intermittent neural networks have proved promise in modeling worldly elements in recordings. Deep video analytics, or video analytics with deep learning, is turning into an arising research territory in the field of pattern recognition.

Pose estimation is another deep learning strategy utilized as a mean for action classification. Action classification is the second group of tasks associated with building computer vision-based surveillance frameworks. When we know the number of individuals we have in the store, and once we understand what they’ve been doing, we can anayze their actions.

Video analytics is used to take care of real-world issues in the city of New York. To all the more likely comprehend significant traffic events, the New York City Department of Transportation utilized video analytics and machine learning to identify parking violations, traffic jams, weather patterns and that’s just the beginning. The cameras catch the actions, process them and send real-time alerts to city authorities.

Deep learning is turning into a fundamental piece of analytic development, and we are consistently finding its ground-breaking applications as research in this field propels. Today, it’s assisting us with taking care of issues more rapidly and more precisely than ever, and even address new difficulties we never expected to overcome.

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Overview of Image and Video Compression

Image and video compression enables you to deliver high-quality media with lower storage and bandwidth requirements. It reduces the size of files, making media easier to transfer and cheaper to store.

To understand how this works, consider any image. Within the image there are many pixels that display the same information, creating spatial redundancies. Compression techniques reduce these redundancies by eliminating or modifying image information. For example, Huffman coding uses entropy coding methods while Discrete Cosine Transform (DCT) uses cosine functions to approximate signal frequencies.

There are a variety of compression standards you can use, depending on the desired outcome. The standard you choose can apply a method such as DCT to identify areas that overlap and eliminate extra data. When this occurs, the quality of media decreases but typically not in a way that viewers can perceive.

This is because high frequencies, such as those created by intense changes or small, sharp details cannot be distinguished by the human eye. The result is that high-resolution images or color spectrums can be reduced to save space.

You can also use methods like DCT on video, however, there are additional methods that you should use as well. For example, interframe compression, which reuses information from initial or subsequent frames to predict frame information. Predictable information can then be eliminated, saving space. Standards that use this method include MPEG and H.264.

Previously, compression was performed only with static algorithms. Recently, however, researchers have developed dynamic compression methods which take advantage of convolutional neural networks (CNNs). These methods use CNNs to perform feature extraction, which can then guide compression algorithms.

What Are Codecs?

Codecs are software or hardware tools containing compression standards. You use codecs to encode and decode data. Codecs are required for compression as well as media viewing or playback.

Some common examples of video codecs are VP9, H.264, and RV40. These codecs are used to modify video streams only, however. To fully compress a video, you also need to use audio codecs, such as MP3, FLAC, or Fraunhofer FDK AAC. In combination, these codecs enable you to compress your videos and all related data, such as audio or title tracks.

One important note about codecs is that a codec is different from a container. Containers are packages that encapsulate all files associated with a video, including video and audio streams, title tracks, metadata, and codecs. Containers are used to interface decoded video data with client players. A container does not dictate how videos are encoded or decoded outside of the codecs it contains. Codecs and containers are frequently confused with each other, because these tools sometimes share the same name. For example, FLAC.

Machine Learning Algorithms for Video CompressionAs AI and machine learning (ML) technologies have advanced, these tools have become useful for improving compression methods. There are multiple ML variations that you can use for compression, but the three main algorithm types are:

• Supervised: Uses predictive capabilities to process and compare large amounts of data in a timely manner. This makes this method well-suited to video processing, making it the most commonly used for encoding and compression.
• Unsupervised: Uses comparison methods to identify similarities in video content. You can use this method to leverage bandwidth economies.
• Reinforcement: Uses feedback loops to refine the algorithm with each successive pass. When these algorithms are used, each adjustment depends on the effects of the previous modification.

The inclusion of machine learning in video compression has the potential to exponentially accelerate the pace of improvement. It can reduce the time needed for compression and the cost of both standard development and processing resources needed.

Machine learning also creates a potential for custom compression algorithms, designed to match each video being processed. This has significant implications for the future of digital media and can help media providers optimize video delivery as they move to the cloud.

The Benefits of Machine Learning and AI for Video Compression

Creating and refining compression tools takes a significant amount of expertise, effort, and time. Integrating AI and machine learning in this process can substantially ease and speed both creation and compression processes. The incorporation of machine learning can provide additional benefits, including:

Faster time-to-market

Machine learning capabilities can enable you to automate compression and codec creation. Using reinforcement learning methods, you can continuously refine both codec development and compression processes with minimal continuing effort. This supported automation helps you optimize your time and speeds overall video processing and delivery.

Improved encoder density

The integration of machine learning enables you to create and use more efficient codecs. Additionally, many machine learning algorithms can be performed in Graphical Processing Units (GPUs) rather than Computer Processing Units (CPUs). GPUs are specialized processors that you can use to perform co-processing in parallel. The use of GPUs can decrease your processing time and improve productivity since CPU power is reserved for other tasks.

Wrapping Up

Current compression algorithms are only able to process a limited amount of data and cannot perfectly correlate patterns. Codecs that incorporate machine learning enable you to process video data with greater detail and accuracy. These tools may even enable you to process data on a pixel-by-pixel level as opposed to frame-by-frame. The result is smaller file sizes with higher quality streams.

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As almost all forms of digital gaming have online elements to them, be it the game being hosted on online servers or customers connecting to the internet via their consoles for head-to-head battles and DLC purchasing, video game industry companies can stack up heaps of data every single day. This includes statistics on how often a certain title is played, what times of the day are most popular with each demographic, the replayability of their titles or even the likelihood that players will purchase additional content.

Starting with such data, gaming brands use complex tools to enhance their products, make marketing decisions, choose the most likely to succeed promotions, or even select the genre and themes of upcoming games. It is safe to say that regardless of the platform in use, gaming is becoming increasingly driven by big data.

Vital in the free-to-play space of mobile gaming

For developers in the mobile gaming sector, big data couldn’t be more crucial to the creation and further development of their IPs. In mobile gaming, especially so in the “casual” genre, the vast majority of games are “free-to-play,” arguably with most people rarely looking at premium mobile games. Despite this, mobile gaming revenues make up nearly half of all global gaming revenue.

The games may be free to play, but developers have discovered ways of monetizing their game space to ensure that their development time isn’t wasted. However, in mobile gaming too, over-monetization or aggressive advertising within a freemium model can turn players off, so companies use big data to measure, predict, and track player behavior to then be able to enhance the experience for the player as well as further encourage in-game spending without going overboard.

A necessary tool for providers to offer the best deals

This point is especially relevant to iGaming, namely the sector that pertains to all real-money games available online. As iGaming is such a competitive field, operators have to utilize big data constantly to try to find ways in which they can enhance their websites, range of games, and, perhaps most importantly, their bonuses and promotions. This can easily be seen in the way that all websites are now organized with the popular slots as their main feature as well as in how bonuses have expanded and become more sophisticated.

By analyzing engagement statistics, times when bonuses are taken, and customer data such as gender and age, platforms that use big data analytics have been able to hone in on the perfect offers for their target audience. For example, the casino bonus in its optimum form now comes as a 100% matched deposit bonus and also includes free spins – as online slots are always among the most popular iGaming titles.

Breaking stigmas in home console gaming

One of the most important breakthroughs to come from the implementation of big data in video gaming is how it has been used to uncover audience splits in home console gaming. It was found that nearly 80% of gamers play games on more than one device – dispelling the notion that most players will buy just one of the home consoles on offer. In turn, this has encouraged game developers and hardware producers to bring in cross-platform online features.

Furthermore, and most importantly, big data finally gave credence to the female audience in gaming, showing that 46% of women indulge in gaming of some form. This was how we learned that there isn’t a significantly dominant audience gender. For decades, there has been the stigma that video gaming is for men, but now developers can explore other avenues to appeal to this huge segment of the player base.

Big data is helping companies all over the world hone in on ways to better serve their respective audiences. In gaming, across all platforms, companies are now being driven by this innovative technology – and the future is set to bring even more of this.

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Washington – A new artificial intelligence tool can create realistic videos from audio files alone. This technology, developed at the University of Washington, has been tested on speeches made by former President Obama.

The technology is based on newly prepared algorithms, which are designed to overcome a limitation with ‘computer vision’. This is with turning audio clips into realistic, lip-synced videos of the person who is speaking the words. The developed algorithms learn from videos that exist “in the wild”, such as on the Internet or elsewhere.

To do so involved training a neural network (a collection of connected units called artificial neurons) to view videos of an individual and then to translate different audio sounds into basic mouth shapes. The second area was using a new mouth synthesis technique to realistically superimpose mouth shapes and textures onto an existing reference video of a given person.

Individual brain cells within a neural network are highlighted in this image obtained by CMU’s Sandra Kuhlman using a fluorescent imaging technique

Carnegie Mellon University

To test out the technology, the research group generated a realistic video of Barack Obama discussing such diverse subjects as terrorism, fatherhood and employment. The video was created using audio clips alone together with a separate video image of the former president. The video overcomes a major problem with adding audio to video, where the mouth of the speaker appears unrealistic.

Discussing the outcome, lead researcher Professor Ira Kemelmacher- Shlizerman enthused: “These type of results have never been shown before.” To this required an artificial intelligence algorithm, one capable of learning and anticipating the intricate patterns of human speech. The reason Obama was chosen for the project was due to the sheer volume of available recordings.

President Barack Obama addresses citizens at a town hall meeting in Santa Monica, California, October 9, 2014

White House

The technology will be presented to the August meeting of SIGGRAPH 2017. A white paper has been produced titled “Synthesizing Obama: Learning Lip Sync from Audio”, to discuss the technology.

What does technology this offer businesses?

The advantages to businesses are considerable, allowing high quality audit recordings to be made and later turned into videos of a higher resolution that would be possible using a standard camera and with taking archival sound recordings, which is an area that may appeal to the entertainments industry. Imagine, for example, being able to hold a conversation with a historical figure in virtual reality by creating visuals just from audio.

What could this mean for you?

For consumers, video chat tools like Skype, Google Hangouts or Messenger will enable any person to collect videos that could be used to train computer models. A further appeal to businesses is since streaming audio over the Internet requires much less bandwidth than video, the new software will put an end to video chats that ‘time out’ as a result of poor connections. This is by reversing the process , that is feeding video into the network instead of just audio. Often with ‘video chats’ the audio is good but the video is poor, which is something that frustrates many business professionals and hampers attempts by businesses to reduce the number of meetings by ‘going digital’.

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