Blind Quality Assessment for Contrast Changed Images
Contrast of image plays an important role in image perception quality and is also susceptive to various factors during image acquisition process.However,only a few image quality evaluation algorithms have been focused on the contrast-changed image quality assessment(IQA),and none of these methods belongs to blind IQA algorithms.Therefore,they cannot be applied to the case when the reference image is not available.Based on the fashionable convolution neural network(CNN),this paper presents a blind contrast-changed image quality evaluation method.First,the distortion image is cropped into patches which are prepared to feed into the network.Then image features used for quality score prediction are extracted by the convolution layers.Finally,a regression layer is applied to map the image features to the space of quality score.Our experimental results suggest that the proposed method is well correlated with subjective evaluations of contrast-changed image quality.
contrast change blind image quality assessment convolution neural network
Dixiu Zhong Ping Shi Da Pan Ming Hou Zefeng Ying Mingliang Han
School of Information Engineering Communication University of China Beijing,China
国际会议
重庆
英文
494-498
2017-10-03（万方平台首次上网日期，不代表论文的发表时间）