QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

• URL: http://arxiv.org/abs/2210.06618v2
• Date: Fri, 14 Oct 2022 13:46:08 GMT
• Title: QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution
• Authors: David Berga, Pau Gall\'es, Katalin Tak\'ats, Eva Mohedano, Laura Riordan-Chen, Clara Garcia-Moll, David Vilaseca, Javier Mar\'in
• Abstract summary: We benchmark state-of-the-art Super-Resolution (SR) algorithms for distinct Earth Observation (EO) datasets. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict quality (as a No-Reference metric) by training on any property of the image. Overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as Loss for optimizing SR predictions.
• Score: 2.425299069769717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both Full-Reference and No-Reference Image Quality Assessment (IQA) metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict quality (as a No-Reference metric) by training on any property of the image (i.e. its resolution, its distortions...) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW which has been developed for evaluating image quality, detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features like blur, sharpness, snr, rer and ground sampling distance (GSD) and obtain validation medRs below 1.0 (out of N=50) and recall rates above 95\%. Overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as Loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.

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