K means Sparse coding Random Samples Raw Image Patch Hard Assignment Soft Assignment
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 (Codebook Representation for No Reference Image Assessment) is tested on LIVE database and shown to perform statistically better than the full reference quality measure, structural similarity index (SSIM) and is shown to be comparable to state of the art general purpose NR IQA algorithms.