No-reference image quality assessment based on deep learning method
In this work,we adopt the use of deep learning method for no-reference image quality assessment.With the development of deep neural networks technology,foundational and deep features of images could be captured without much prior knowledge.So a sparse autoencoder(SAE)was trained to express a 32 x 32 pixels image into a feature vector.Then the original images were cut into serial sub-images with the size of 32 x 32 pixels which can fix the input size of SAE.After that,the features vector of each sub-image was extracted separately and the information was fused with two strategies for the image quality assessment task.The best strategy in this work is that each sub-score is calculated by a Support Vector Regression(SVR)machine with the input of sub-image feature vector and estimate the image quality by averaging the scores to get the final score for the original image.Moreover,the effectiveness of our proposed method was confirmed by the experimental results in the TID2013 image quality assessment database.
Deep learning sparse autoencoder no-reference image quality assessment (NF-IQA)
Ruozhang Yang Jiangang Su Wenguang Yu
Beijing Institute of Tracking and Telecommunication Technology,Beijing,100094,China
国际会议
重庆
英文
476-479
2017-10-03（万方平台首次上网日期，不代表论文的发表时间）