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Drive Video Intelligence across the Content Value Chain with AI-led Metadata Tagging

In March 2022, Americans spent roughly 20 billion hours watching video content. In other words, 61 hours of video content were consumed per capita. Today, the global streaming software market is valued at $170 billion and is slated to grow with a 12.1% CAGR until 2028. As a result, it will be worth $932 billion by the end of the prediction period. 

The rising tide of video content is not a recent phenomenon. Video content permeates every aspect of our lives across geographies and demographics, and the upward trend is intuitively in line with our experiences as an audience. As the media and entertainment industry grows, the need to understand viewership trends and patterns is in rising demand. Technologies that emphasize viewership-based personalization are driving both engagement and monetization. 

Automation and Hyper-personalization: The Future of M&E

According to a Deloitte report in 2021, subscription churn averages around 30% in the USA market. As a result, content creators/producers need to provide more personalized ways to appeal to consumers and stay ahead of the competition. Media houses must aim to speak to audiences by analyzing and acting on data based on viewership patterns. 

Advertising forms an integral part of the M&E industry. Significant resources are invested to advertise and promote brands on streaming platforms. In order to assess the impact of commercials based on parameters such as brand visibility and its corresponding impact on revenue, considerable time and effort are required by way of human annotation of brand-specific information. As the need for time-saving processes gains ground, it is imperative for decision-makers to automate processes and reduce manual efforts. 

In today’s economy, ads are considered an unpleasant interruption in the viewing experience with over 66% of people choosing to skip online video ads. However, that is not true for contextual advertising. Video intelligence enables OTT platforms to target specific moments in line with the content and prevent tone-deaf ads. These include the following use cases:

  • Contextual ad-placements
  • Determining ad-compliance 
  • Brand visibility for media and television 
  • Hyper commercialization – blending OTT with e-commerce

Custom-built AI/ML video intelligence solutions assist in boosting the effectiveness of contextual advertising, enhancing the productivity of content storage teams by automatically detecting duplicate videos and efficiently managing content archives. Content hyper-tagging is vital for effective management as it makes searching content easier by using metadata tags. Additionally, it can help monetize archived content and improve its reusability, drive deeper business intelligence, provide accurate financial reporting, and create new user experiences. Custom-built AI can further help generate relevant and contextually rich metadata by combining video intelligence with business needs.

Metadata: The data behind the data

Video intelligence involves combining computer vision with business acumen to solve niche media industry challenges. Depending on the content – TV Shows, News, or Web series – AI offers a contextual description of content through metadata and uses this understanding to solve the content objectives. 

Metadata is a type of descriptive data that helps a person or computer identify the characteristics of a file. Simply put, metadata refers to people, objects, locations, dialogues, keywords, or any useful concept that meaningfully describes the constituents of content. While metadata is an important aspect for all types of content and files, its true benefits are observed with comprehensive video metadata for video content. 

Descriptive metadata
Photo: Home Box Office Inc., all right reserved

With the recent developments in AI and cloud technology, businesses can now process content at scale. Media houses can generate highly granular time-coded streams of metadata that explain the content structure with the desired details. This metadata is further used to create a meaningful taxonomy of content entities. However, it is necessary to understand that video intelligence isn’t only about generating volumes of metadata. Its purpose is to create meaningful metadata that describes your content per the desired use case. The effort of metadata creation must justify its business utility.

There are three main types of metadata, each with a slightly different use case and often focused on content accessibility:

  • Descriptive metadata: This type of metadata includes information such as keywords, objects, places, actions, file names, and authors. This is the common association of metadata and aids in content discovery, identification, and retrieval. 
  • Structural metadata: Structural metadata tags are intended to show the big picture of how content is associated with other assets. This data defines a structure for the content and guidance on the relationship to other elements. 
  • Administrative metadata: This type of data is used to help manage a resource, sometimes used to dictate access rights or background information on the asset. Primarily, these metadata tags contain information such as the file type and date of creation. 

Media houses can leverage the capabilities and opportunities provided by metadata tagging to enable powerful content discovery and engaging video content experiences. Let’s take a look at some of the applications of metadata tagging:

  • Content Regulation: Metadata tagging empowers organizations to identify videos with inappropriate content. Instant content moderation across petabytes of data can be executed, allowing for quick and efficient filtration of content or user-generated material. 
  • Recommendation Engines: Content recommendation engines use labels produced by the Video Intelligence API along with the user preferences and viewing history to deliver relevant content. 
  • Indexing media archives: Metadata generated by the Video Intelligence API is utilized to create an indexed archive of video collections. Ideal for mass media enterprises, the Video Intelligence API can automatically analyze content and deliver instantaneous insights accessible via the API. 
  • Contextual advertising: Commercials that are contextually relevant to the video content can be inserted in the right places in videos. This can be achieved by aligning the content of the adverts with the timestamp-specific labels of the video content.

Quantiphi’s Video Intelligence Solution

Quantiphi has combined computer vision, deep learning, and AI techniques to develop a bleeding-edge metadata tagging solution to make content more discoverable. 

Training: Custom Model Development

  • Video tagging and generation of custom tags and data
  • Training models on a custom-built AI platform
  • Customization of pipelines based on use cases and business requirements

Inferencing: AI-ML Inferencing for Media Applications with Contextual Metadata

  • Extraction of valuable metadata about the video and its content
  • Processing the metadata to conform to the predefined business rules for a use case/application
  • Integration with Media Asset Management (MAM) systems to write metadata and make archives smart
  • Active learning feedback loops to customize the pipeline for ever-changing requirements/rules and newly added tags/data

Management: One-Stop-Shop Platform for Media Workflow Operations

  • Application of meta-tags on the content 
  • Enabling deep metadata-based search
  • Easy content editing, repurposing, and publishing 
  • Integration into mission-critical content analytics workflows
AI across the content value chain

Success Story: Enriching Content Archives with Searchable Video Metadata

Problem Context: Sportscast, a subsidiary of Deutsche Fußball Liga (DFL), needed AI-enriched metadata to archive decades of football content. 

Quantiphi Solution: Quantiphi developed a bespoke video intelligence platform to generate editorial-worthy moments in the DFLs content library.

Impact:

  • 400+ hrs of historic content processed via automatic archiving
  • 1500+ players and DFL members added to the platform
  • 1.5M data points encompassing league players, camera shots, logos, and custom entities

Quantiphi’s cutting-edge AI capabilities across the media value chain empower content owners with ample tools to innovate and differentiate. Our media intelligence offerings help media houses seamlessly adopt AI into their workflows and improve productivity, produce higher quality content, streamline operations, uncover deep content insights and unlock scalable revenue-generating business models.

Get in touch with our experts to learn how media houses can leverage AI-led video intelligence to maximize value creation from their video content.

Written byAditya Sharma

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