Topic of your interest

Machine Learning Framework for dynamic image style evaluation

Short name: DeepQuality

Fogra no. 13.004
Project leader: M. Wimmer (Fogra) and Prof. Dr D. Merhof (LfB)
Partner: Institute of Imaging & Computer Vision at Aachen University
Funding: BMWK (IGF) via AiF
 

Timescale: 01.06.2020 - 31.05.2022

The selection of suitable images for a given image style (also called briefing) from a large number of source images, e.g. from a photo shoot, always means a great deal of time and effort for the agency or repro staff involved.

Precisely targeted image data (original and corrected images) that are characteristic for a specific style are the basis for the development of a neural network that enables a dynamic image quality assessment without a formalized (human) description of the respective taste or image style. The evaluation is carried out in such a way that any images for a given output method are automatically checked to see whether they correspond to a certain image style and how much effort may be required for a necessary retouching (image correction; “traffic light system”). 

Working hypothesis is that “image-to-image translation” as well as “image-to-feature translation” are capable of developing such a traffic light system. Since the decisions on the suitability of image data are already made manually by experts, sufficient training data is available. This approach is further based on the hypothesis that the recognition of the relevant features for gradual agreement with an image style is sufficient to successfully apply this style to new images – and thus to correct them automatically. This last step is particularly demanding and represents the greatest challenge within the project.

Martin Wimmer

Prepress Technology

+49 89 431 82 - 411

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Targeted results

With the help of machine learning and on the basis of artificial intelligence, this research project aims to make it easier to assess the suitability of images for a certain style of image. 

Furthermore, a web app should make it possible to learn features and image style based on images that cannot be used for evaluation due to data protection or lack of copyright. 

Meeting documents