GSoc/2022/StatusReports/PhuocKhanhLe

DigiKam Image Quality Sorter Algorithms Improvement

The main idea of IQS in digiKam is to determine the quality of an image and convert it into a score. This score is based on four factors sabotaging image: blur, noise, exposure, and compression. The current approach helps determine whether images are distorted for one of these reasons. However, the current algorithm also presents some drawbacks: It demands lots of fine-tuning from the user’s side and cannot work on the aesthetic image. So, I propose the solution of the deep learning algorithm. While the dataset and the paper for aesthetic image quality assessment are free to use, we are capable of constructing a mathematical model that can learn the pattern of a dataset, hence, predicting the score of quality. As deep learning is an end-to-end solution, it doesn’t require the setting for the hyperparameter. Therefore, we can reduce most of the fine-tuning parts to make this feature easier to use

Work report

25/07/2022: Conclusion of first model for Aesthetic Image Quality Assessment.

07/08/2022 : Feature Aesthetic Detection to classify aesthetic image in digiKam using deep learning model.

30/08/2022 : Improvement of the model for Aesthetic Image Quality Assessment.

Links to Blogs and other writing

Main merge request:

• https://invent.kde.org/graphics/digikam/-/merge_requests/181

My blog for GSoC :

• https://phuockhanhle.github.io/jekyll/update/2022/06/19/gsoc-2022.html

Research repository for aesthetic image quality assessment:

• https://github.com/phuockhanhle/iqs-digikam