On the Use of Deep Learning for Blind Image Quality Assessment

TL;DR

This paper explores deep learning techniques for blind image quality assessment, proposing a new model called DeepBIQ that outperforms existing methods by effectively predicting image quality scores aligned with human perception.

Contribution

The paper introduces DeepBIQ, a novel deep learning-based approach that combines CNN features and SVR for improved blind image quality assessment.

Findings

DeepBIQ achieves high correlation with human scores (LCC ~0.91 and 0.98).

DeepBIQ outperforms state-of-the-art methods on benchmark datasets.

Predictions of DeepBIQ are often closer to average human observer scores.

Abstract

In this work we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average pooling the scores predicted on multiple sub-regions of the original image. The score of each sub-region is computed using a Support Vector Regression (SVR) machine taking as input features extracted using a CNN fine-tuned for category-based image quality assessment. Experimental results on the LIVE In the Wild Image Quality Challenge Database and on the LIVE Image Quality Assessment Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a Linear Correlation Coefficient (LCC) with human subjective scores of almost 0.91 and 0.98 respectively. Furthermore, in most of the cases, the quality score predictions of DeepBIQ are closer to the average observer than those of a generic human observer.