In the MeMad project, we’re working with the following use cases:
UC1: Content delivery services for the re-use by end-users/clients through media indexing and video description
Online media delivery platforms rely heavily on media metadata in supplying, recommending and grouping digital media to clients. This use case aims to enhance the end-user experience of such services by creating and making use of rich metadata and hyperlinking created by automated media analysis and multimodal media indexing.
As a result, users of such delivery services should be able to discover and watch media that are meaningful to them from a spectrum of starting points and interests that is significantly broader than what can be achieved by current methods of metadata creation. Users should, for example, be able to browse and discover themes, people and places from media, and parts of media containing these even when the information has not been entered by production staff or the original media product has been designed for a different purpose.
Performance can be measured by monitoring the time spent in a service and number of media acceses (from simply startedstarts to fully consumed) based on recommendations and successful searches.
UC2: Creation, re-use and re-purposing of the new footage and archived content in digital media production through media indexing and video description
This use case aims to improve discoverability and re-usability of digital-born as well as pre-existing media for the purpose of crafting new stories and audiovisual concepts. Media professionals are provided with rich and relevant relationships between archive media, scripts and raw footage during different stages of digital media production, enabling them to develop a digital story and concepts with the help of automated metadata extraction and media analysis. Relevant media fragments are automatically recommended, which saves significant amounts of editorial work compared with conventional methods of research in media archives.
Performance can be measured by monitoring production costs and productivity of archive research and footage management.
UC3: Improving user experience with media enrichment by linking to external resources
A video program may be edited using a complex narrative but viewers have different background and interests and may not be familiar with all the elements being presented, triggering the need to go more in depth for some aspects being presented. Video programs also trigger social media reactions (e.g. on Twitter or Facebook) where sometimes viewers clip and repurpose some original parts of the video program. One way to improve the user experience is to provide individual users the possibility to access and explore related material (e.g. videos, news articles or set of facts extracted from encyclopedia) that will contain additional information that they personally need or are interested in to better understand the narrative of the video program.
External material may be essential for understanding the audiovisual content. For example, when republishing decades old audiovisual content from the archives, to understand the meaning of the archive content, additional material may be required that gives the historical context and information on how to interpret the content.
Performance can be measured by monitoring the amount of additional content explored by users while accessing a video program.
UC4: Automated subtitling/captioning and audio description
This use case addresses an urgent requirement to enhance as much content as possible with complementary subtitles and aural audio description. Conventionally these are created by human subtitlers and translators, and at a total production cost of 1000-1200 Euro per hour (for subtitling) up to 3000 Euro per hour (for audio description). Also, manual subtitling and audio description requires a significant cycle time from one to two weeks.
For this use case, we will undertake to maximise productivity of both subtitling (same language as well as language to language) and audio description processes, through “supervised automation”. Performance is measured by the decreased cost per hour of processed material and decreased cycle time that result from: (1) increasing automation level and multilingualism, (2) enhancing information present in subtitles and audio description, and (3) reducing the average production costs for subtitling and audio descriptions by increasing the percentage of content provided with machine-generated subtitles and audio descriptions.