Feng Yuan, Xiao Shao*
Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357
Abstract Traditional image quality assessment methods use the hand-crafted
features to predict the image quality score, which cannot perform well in many
scenes. Since deep learning promotes the development of many computer vision
tasks, many IQA methods start to utilize the deep convolutional neural networks
(CNN) for IQA task. In this paper, a CNN-based multi-scale blind image quality
predictor is proposed to extract more effectivity multi-scale distortion features
through the pyramidal convolution, which consists of two tasks: A distortion
recognition task and a quality regression task. For the first task, image distortion
type is obtained by the fully connected layer. For… More >
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