Blind Natural Image Quality Prediction Using Convolutional Neural Networks And Weighted Spatial Pooling

Abstract

Typically, some regions of an image are more relevant for its perceived quality than the others. On the other hand, subjective image quality is also affected by low level characteristics, such as sensor noise and sharpness. This is why image rescaling, as often used in object recognition, is not a feasible approach for producing input images for convolutional neural networks (CNN) used for blind image quality prediction. Generally, convolution layer can accept images of arbitrary resolution as input, whereas fully connected (FC) layer only can accept a fixed length feature vector. To solve this problem, we propose weighted spatial pooling (WSP) to aggregate spatial information of any size of weight map, which can be used to replace global average pooling (GAP). In this paper, we present a blind image quality assessment (BIQA) method based on CNN and WSP. Our experimental results show that the prediction accuracy of the proposed method is competitive against the state-of-the-art image quality assessment methods.