Intelligent image and video management for the enterprise

As image and data quality goes up – driven by better capture, transfer, processing and compressing technologies – the ability to store and process these images and video data easily becomes a competitive advantage.

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An often-overlooked aspect of enterprise data management is the acquisition, storage and usage of image and video data. An enterprise can generate tons of images depending on the industry it is in or how digital it is. For example, the healthcare vertical generates terabytes of images or physical security enterprise can generate large amounts of video files. As image and data quality goes up – driven by better capture, transfer, processing and compressing technologies – the ability to store and process these images and video data easily becomes a competitive advantage.

Key requirements for Image & Video management in the enterprise include the ability to transfer images and videos from the point of creation to the point of processing and subsequently to the point of consumption. In addition, image and video data also requires organization and storage to support the data processing and consumption patterns. A common mistake is to treat image and video data as just data and use traditional log/analytics/file storage mechanisms. Not only are those approaches not ideal for images and videos but also can reduce the scope and type of value that can be generated and delivered to end users and customers.

Intelligent image & video management

AI has the potential for a huge impact on how images and video are processed. Deep learning has already been proven to be a huge disruptor of image processing and has revolutionized how images are classified and how objects and situations in images are recognized. Similar advances are being made in video processing with several research projects focused on detecting objects, entities and intent in videos. In addition, video data is being used to learn sequences of action or transitions in video to be able to better predict the next scene in a video. The ability to transcribe sound from videos is also getting a boost with the application of machine learning to hear, transcribe and translate the sound track on videos.

The net effect of the application of AI to images, video and sound processing means that enterprises can reduce their operational costs for storing and processing images, video and sound. In addition, AI driven search and retrieval capabilities can lead to more efficient organization to ensure that the optimal data is searchable and usable.

Metadata driven organization

Machine learning and other AI technologies can generate metadata that makes it easier to classify, categorize and describe image and video data. Metadata such as objects, entities (people, places, things) can be detected and leveraged as metadata. Similarly, geotagging of images and videos can be used to determine the location associated with the image/video. Image and videos can also be summarized using various summarization techniques making it easier for a human to consume them.

Image & video optimization

Image and Video optimization is another area where advanced processing can ensure that the data is stored with the optimal format, compression and quality. It is critical that data be appropriately transformed and compressed to reduce storage costs but at the same time is readily available for consumption and processing when required. Companies like Cloudinary or AWS CloudFront offer really innovative capabilities that offer the right balance between image optimization for storage and optimization for interactive processing and delivery.

Image & video distribution

Data driven distribution strategies can ensure that the most relevant and appropriately formatted images and videos are surfaced to the user but are done so to ensure that the user experience is optimal and delightful. For example, enterprises can use web analytics to determine how their page load time on their websites during search or image rendering experiences vary with various image sizes, compression and quality and dynamically adjust the delivered images based on predicted threshold levels beyond which user experience suffers.

Another technique that can be used by enterprises is to use machine learning driven object and entity recognition to deliver images and videos where the key objects and entities are rendered with high quality and other non-critical information can be rendered at lower quality. In addition, metadata about the image and video extracted through intelligent processing can also be greatly enhance the user experience by providing meaningful context and annotations making the images and video content easier to search, discover, leverage and use.

Cloud or not?

Another interesting question that enterprises have to wrestle with is whether to move their image and video data storage and distribution to the cloud or manage their own mechanisms. The typical “cloud or not” arguments are still valid and thus this question can be quite perplexing for enterprises.

Depending on the size, complexity of data and competencies of the enterprises, the decision should be taken to ensure that the enterprise spends its time adding to the user experience by analyzing, processing and intelligently delivering image/video content rather than getting distracted by the technology and infrastructure required to store and organize data; efforts that might not have a direct impact on user experience and value.

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