A Python Framework for Objective Visual Quality Assessment - Saigg, Caio L.; Dias, Bruno S. S.; Costa, André H. M.; Farias, Mylène C. Q.; Martinez, Helard B. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2022: Anais Estendidos da XXXV Conference on Graphics, Patterns and Images, 2022)
This work introduces a Quality Assessment Framework that provides researchers with the flexibility, consistency, and scalability they need to evaluate and compare quality metrics, promoting the reproducibility of results. The framework is open source (Python) and currently has 11 visual quality metrics that use 3 different libraries: Scikit-video, FFmpeg toolkit, and PyMetrikz. It can be easily expanded to include more metrics in the future and allows testing on several quality datasets. To validate it, we tested it on two datasets and compared the results with the results obtained by other authors in the literature. The results are consistent with those reported by external studies. With this evidence, new image/video metrics and datasets can be integrated into this framework. This will allow researchers to compare their methods with a wide number of quality metrics on several datasets in a fast and efficient way.