@Article{jbd.2020.015357,
AUTHOR = {Feng Yuan, Xiao Shao},
TITLE = {Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {167--176},
URL = {http://www.techscience.com/jbd/v2n4/41034},
ISSN = {2579-0056},
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 the second task, the image quality 
score is predicted during the distortion recognition progress. Experimental results 
on three famous IQA datasets show that the proposed method has better 
performance than the previous traditional algorithms for quality prediction and 
distortion recognition.},
DOI = {10.32604/jbd.2020.015357}
}