Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images

TL;DR

This paper introduces a no-reference image quality assessment method that leverages high-level semantic features from deep neural networks to more accurately evaluate the quality of realistic blur images, outperforming existing methods.

Contribution

The novel approach exploits high-level semantic features and statistical aggregation for improved no-reference image quality assessment of realistic blur images.

Findings

Outperforms state-of-the-art methods on realistic blur databases.

High-level features are more effective than low-level features for quality prediction.

Achieves comparable performance on synthetic blur image datasets.

Abstract

To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably. The neglect of the high-level semantic information may result in predicting a clear blue sky as bad quality, which is inconsistent with human perception. Therefore, in this paper, we tackle this problem by exploiting the high-level semantics and propose a novel no-reference image quality assessment method for realistic blur images. Firstly, the whole image is divided into multiple overlapping patches. Secondly, each patch is represented by the high-level feature extracted from the pre-trained deep convolutional neural network model. Thirdly, three different kinds of statistical structures are adopted to aggregate the information from different patches, which mainly contain some common statistics (i.e., the mean\&standard deviation, quantiles and moments). Finally, the aggregated features are fed into a linear regression model to predict the image quality. Experiments show that, compared with low-level features, high-level features indeed play a more critical role in resolving the aforementioned challenging problem for quality estimation. Besides, the proposed method significantly outperforms the state-of-the-art methods on two realistic blur image databases and achieves comparable performance on two synthetic blur image databases.