# Robust statistics and no-reference image quality assessment in Curvelet domain

## TL;DR

This paper introduces a new no-reference image quality assessment model using robust statistics and curvelet transform, demonstrating improved performance over previous methods across multiple datasets.

## Contribution

The paper presents a novel NR IQA model called M1 that leverages robust statistics with curvelet features, outperforming the 2014 Curvelet2014 approach.

## Key findings

- M1 outperforms Curvelet2014 in quality prediction accuracy.
- Robust statistics improve feature reliability in IQA.
- Statistical tests confirm the significance of results.

## Abstract

This paper uses robust statistics and curvelet transform to learn a general-purpose no-reference (NR) image quality assessment (IQA) model. The new approach, here called M1, competes with the Curvelet Quality Assessment proposed in 2014 (Curvelet2014). The central idea is to use descriptors based on robust statistics to extract features and predict the human opinion about degraded images. To show the consistency of the method the model is tested with 3 different datasets, LIVE IQA, TID2013 and CSIQ. To test evaluation, it is used the Wilcoxon test to verify the statistical significance of results and promote an accurate comparison between new model M1 and Curvelet2014. The results show a gain when robust statistics are used as descriptor.