Streamlining Image Management with Automated Metadata Extraction

As a global company, we understand the importance of efficient image management. With millions of images to maintain and continually update, manually grouping and organizing these assets can be both time-consuming and expensive. To overcome this challenge, we developed a process that leverages technology to extract metadata from images more efficiently.

Our collection of images has undergone various transformations, including cropping, resizing, color correction, and title treatment, to cater to a global audience. While visually similar, these images do not all originate from the same source. Moreover, we have multiple derivatives of the same image to display for different users. To gain a better understanding of how well a particular set of visually similar images performs globally, it is crucial to group them together based on their visual similarity.

To achieve this, we rely on the JP2 Glymur library, which allows us to extract metadata from JPEG 2000 files. Specifically, we are interested in the metadata contained within the fourth element of the box in the JP2 file. Using glymur, we have successfully extracted the XML Etree and gained insights into the metadata stored within these images.

One of our primary goals is to update the metadata in a series of JPEG 2000 images. However, when inspecting the EXIF header using GIMP or ImageMagick's identify image.tiff command, the metadata does not appear to be present. This has led us to explore alternative methods for retrieving the metadata.

In addition to managing visual assets, we also implement metadata on the images themselves. The manufacturer of our SEM equipment provides metadata within the file itself, which we have struggled to retrieve successfully. To address this challenge, we are continually testing various flavors of artwork for different titles to determine which one performs best globally.

The tool we developed focuses on extracting metadata from EXIF images (i.e., JPEG files), but can also extract metadata from almost every common media format. By leveraging this technology, we can streamline our image management process, gaining valuable insights into the performance of our visual assets across different regions and audiences.

Overall, extracting metadata from images has become a crucial step in our global company's efforts to optimize its visual content. By automating this process, we can reduce costs, increase efficiency, and make data-driven decisions about which images perform best globally.

Extracting metadata from images