Yu Shaode, Wu Shibin, Wang Lei, Jiang Fan, Xie Yaoqin, Li Leida
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
PLoS One. 2017 May 1;12(5):e0176632. doi: 10.1371/journal.pone.0176632. eCollection 2017.
Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). The network takes single feature layer to unearth intrinsic features for image sharpness representation and utilizes multilayer perceptron (MLP) to rate image quality. Different from traditional methods, CNN integrates feature extraction and score prediction into an optimization procedure and retrieves features automatically from raw images. Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR). Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment.
盲图像质量评估可以建模为特征提取后进行分数预测。手工制作用于感知图像质量最优表示的特征需要相当多的专业知识和努力。本文通过使用浅层卷积神经网络（CNN）来解决盲图像清晰度评估问题。该网络采用单个特征层来挖掘用于图像清晰度表示的内在特征，并利用多层感知器（MLP）对图像质量进行评分。与传统方法不同，CNN将特征提取和分数预测集成到一个优化过程中，并从原始图像中自动检索特征。此外，通过用广义回归神经网络（GRNN）和支持向量回归（SVR）取代MLP，可以提高其预测性能。对来自LIVE-II、CSIQ、TID2008和TID2013的高斯模糊图像进行的实验表明，具有SVR的CNN特征实现了最佳的整体性能，表明与人类主观判断具有高度相关性。
