Unsupervised feature learning framework for no-reference image quality assessment

In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to build a computational model to automatically predict human perceived image quality without a reference image and without knowing the distortion present in the image. Previous approaches for this problem typically rely on hand-crafted features which are carefully designed based on prior knowledge. In contrast, we use raw-image-patches extracted from a set of unlabeled images to learn a dictionary in an unsupervised manner. We use soft-assignment coding with max pooling to obtain effective image representations for quality estimation. The proposed algorithm is very computationally appealing, using raw image patches as local descriptors and using soft-assignment for encoding. Furthermore, unlike previous methods, our unsupervised feature learning strategy enables our method to adapt to different domains. CORNIA...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Peng Ye, Jayant Kumar, Le Kang, David S. Doermann
• Keywords: Computer Vision, Cvpr 2012, Image Quality Assessment, Quality Estimation, Unlabeled Images