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

TL;DR

This paper benchmarks super-resolution algorithms for Earth Observation images, introduces QMRNet for no-reference quality prediction, and demonstrates its effectiveness in optimizing and assessing image quality.

Contribution

It presents a novel QMRNet model for predicting image quality without reference images and integrates it into a framework for EO image analysis.

Findings

QMRNet achieves validation medRs below 1.0 for quality prediction.

High recall rates above 95% for quality metrics.

Promising results for super-resolution methods LIIF, CAR, and MSRN.

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.