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Spatial Moment Pooling Improves Neural Image Assessment by Xu, Tongda; Shao, Yifan; Wang, Yan; Qin, Hongwei is a scholarly article available to read on EtoBox.

What is Spatial Moment Pooling Improves Neural Image Assessment about?

In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA). A large number of works start by extracting deep features from CNN. Then, those features are processed through spatial average pooling (SAP) and fully connected layers to predict quality. Inspired by full reference IQA and texture features, in this paper, we extend SAP ($1^{st}$ moment) into spatial moment pooling (SMP) by incorporating higher order moments (such as variance, skewness). Moreover, we provide learning friendly normalization to circumvent numerical issue when computing gradients of higher moments. Experimental results suggest that simply upgrading SAP to SMP significantly enhances CNN-based blind IQA methods and achieves state of the art performance.

Author

Xu, Tongda; Shao, Yifan; Wang, Yan; Qin, Hongwei

Published

2022

Language

EN