Innovation is the heartbeat of the OTT industry. It’s not just about what’s on the screen but how it engages users. Creative storytelling and smooth user experience keep audiences captivated. Interactive plots, personalized content recommendations, automated processes of content creation – these have been our reality for years. Do you know how to use it?

From script to screen. AI in the video production and OTT industry

Generative AI (Artificial Intelligence) is a subset of artificial intelligence with the capability to generate data. Traditional AI focuses on solving specific tasks, Generative AI is a huge help in creating entirely new and authentic data. AI can produce new images, texts, or even sounds, opening doors to unprecedented creativity and adaptability.

Content Creation

Automated script generation

AI-driven video editing and production

AI-driven automation optimizes tasks such as video editing, improving efficiency and reducing manual effort in the post-production phase. For example, it can analyze video content, identify the key scenes, and automate editing decisions based on predefined criteria. It makes production faster and more consistent. Plus, it lets us try different editing styles to match what viewers like on OTT platforms.

Automatic dubbing

Generative AI services use advanced technology to automatically transcribe, translate, and dub audio and video content. Large language models (LLMs) are exposed to a diverse range of linguistic patterns, vocabulary, and structures from various languages. They can precisely convert spoken words, translate them into various languages, and create synchronized dubbing. This innovation is helpful for media companies, content creators, and language service providers as it simplifies their work processes and allows them to connect with audiences around the world more efficiently.

Content redaction

Tasks such as identifying the beginning of end credits, strategically placing ads, and segmenting videos for improved indexing are not only expensive and slow but also struggle to keep up with the increasing volume of content. Simultaneously, activities like subtitling and extracting metadata are becoming more time-consuming and labor-intensive. There are plenty of more productive ways to use working time than burning it on tagging, labeling, captioning, and reviewing media assets.

Machine learning in content redaction – examples

Amazon Transcribe offers automated speech recognition. Its machine-learning models can automatically figure out the main language in an audio file and create precise transcriptions. It’s a versatile tool useful for tasks like making subtitles automatically or targeting ads based on spoken content.

Another example is Amazon Rekognition. This cloud-based service provides advanced image and video analysis. It utilizes machine learning models to identify and analyze objects, scenes, and activities in live video streams. It simplifies video editing by automating Scene and Object Recognition, Facial Analysis, and Text Detection. 

NASCAR employs Amazon Rekognition for the automatic tagging of video frames with detailed metadata, including driver, car, race, lap, and time. The Amazon SaaS works also with data from sensors tracking more than 60 data points. From engine RPMs to brake pressure. Additionally, Amazon Transcribe is utilized for captioning and timestamping speech, saving NASCAR extensive manual tagging and searching in their video archives. To further enhance metadata and video analytics, they utilize Amazon SageMaker to train deep learning models. This efficient approach ensures the presentation of the most immersive car-racing experience.

Optical Character Recognition (OCR) utilizes machine learning to extract text from images or videos, enabling real-time analysis for generating captions and metadata.

Automated close captioning

Metadata enhancement

Metadata refers to the additional information or descriptors that provide context and details about the content, helping users discover, understand, and navigate through the vast amount of available content on OTT platforms.

Creating new content from enhanced metadata involves leveraging the enriched information associated with existing content to generate additional value, whether through content discovery, user engagement, or even the development of new experiences. Here are some ways in which enhanced metadata can contribute to the creation of new content:

1. Curated playlists and collections: Platforms can use metadata to curate themed playlists or collections based on various criteria such as genre, mood, themes, or historical context. This curated content can attract users looking for a specific type of viewing experience and introduce them to new content.
2. Content mashups and remixes: By analyzing metadata, platforms can identify patterns, themes, or elements that resonate with users. This information can be used to create content mashups or remixes, combining elements from different pieces of content to produce something new and unique.
3. User-Generated content challenges: Platforms can leverage enhanced metadata to organize user-generated content challenges or contests. Users could be encouraged to create content based on specific themes, genres, or creative prompts derived from metadata, fostering a sense of community and interaction.

Automated Highlight Selection

Automated Highlight Selection is a technique that facilitates the identification and presentation of key moments in multimedia material. Auto Highlight is used to automatically extract highlights from a clip. This tool saves time during video editing, allowing for the quick extraction of the best parts of the material.

Thanks to automated highlight selection, content creators can expedite the editing process and more easily tailor content to meet the expectations of their audience. This is particularly important in an era where audience attention is limited, and the rapid conveyance of key information becomes a priority.

All mentioned examples facilitate content management by automating tasks such as identifying technical cues for ad insertion and shot detection for promotional video creation. This eliminates the necessity for slow, manual, and costly processes, ensuring efficiency in handling the increasing volume of daily-produced, licensed, and archived content. Simultaneously enhancing the user experience. Now, delving into the realm of user experience.

User Experience Enhancement

Everyone already knows that video content is clickable. Users consume 17 hours of video content per week. But here’s the thing – they consume. The semantics of OTT revolve around eating. “Binge-watching”, and “easily digestible content” – examples abound. However, let’s focus on this digestibility because that’s what made video so popular.

Requiring less effort from the user, more options for evoking emotions, and plenty of options for improving accessibility. That is why we love video content. And that’s exactly why the entire user experience (UX) was created—to make these contents as enjoyable and easy to consume as possible. ChatGPT would say “smooth and seamless” and he would be right – because that’s the main point.

We’ve already mentioned that the content redactors have a lot on their plate, and their actions elevate the UX for end users. Let’s take a closer look at how UX and Machine Learning connect.

Personalized recommendations using Generative AI

To meet ever-changing expectations swiftly, outshine the competition, minimize churn, and boost retention OTT platforms need a strategy. Many companies opt for personalization as a solution, employing advanced machine learning algorithms to assess user preferences, viewing history, and behavior. This analysis drives the generation of personalized content suggestions, aligning recommendations with individual interests and viewing habits. For instance, the system understands genre preferences, recommends similar content, and predicts future preferences based on historical data. This level of personalization contributes to increased user engagement, satisfaction, and longer viewing sessions on OTT platforms.

You can find more about it in the article: content recommendations and machine learning.

Advanced user interfaces and interactive content

Generative AI makes it easier to create interactive content features. Users can engage with content through personalized quizzes, polls, or choose-your-own-adventure experiences, making the viewing experience more immersive and enjoyable.  

One notable aspect is the utilization of machine learning for metadata enhancement. Through this process, the production output can be transformed into engaging previews and highlights. The content is passed through Amazon Transcribe, converting spoken words into text. Subsequently, this textual data is fed into GPT (a powerful language model probably known to everyone), which generates compelling and informative film descriptions.

Benefits of using Generative AI in the OTT

Time and creativity unleashed

Generative AI automated content creation processes, allowing individuals to work faster and allocate their creativity to different tasks. Subtitles, dubbing, new video production? AI’s got your back, saving time and resources. It’s like a digital assistant without the coffee demands. You can use it to create videos, sounds, music, and graphics, reducing the time and resources required for manual work.

Increased accessibility of content

Generative AI seamlessly generates subtitles and dubbing, ensuring your content reaches a diverse global audience. By prioritizing language translation and accessibility, OTT platforms reach completely new markets without gigantic up-front investment, boosting revenue.

Conclusions

The integration of AI and Generative AI in OTT platforms automates content creation, improves accessibility with features like automated dubbing and closed captioning, and enhances user experience through personalized recommendations and interactive content. This technological synergy promises a more dynamic and engaging future for the streaming industry.

Machine learning helps make things run smoother inside a company, saving money, boosting productivity, and letting the team focus more on customers. With fewer mistakes, editors have less work, and overall costs go down.

How Insys Video Technologies leverage AI and ML solutions?

At Insys Video Technologies, we listen to our customers and observe the market. As a response, we made the Cloud Video Kit. The all-in-one tool for video processing, management, and distribution.

Cloud Video Kit roadmap for 2024 includes incorporating speech-to-text, translations, and potentially media insights.

As a result, we will simplify the integration of AI and ML technologies for OTT purposes. It will improve user experiences through features like personalized recommendations powered by Generative AI, advanced user interfaces, and interactive content.

Our commitment is to provide our clients with the best tools and integrations, ensuring innovation and staying abreast of industry trends. We actively respond to market needs, anticipating and meeting expectations to make our clients’ work more efficient and enjoyable.