No-Reference Image Quality Assessment using Visual Codebooks

The goal of no-reference objective image quality assessment (NR-IQA) is to develop a computational model that can predict the human perceived quality of distorted images accurately and automatically without any prior knowledge of reference images. Most existing NR-IQA approaches are distortionspecific (DS) and are typically limited to one or two specific types of distortions. In most practical applications, however, information about the distortion type is not really available. In this paper, we propose a general-purpose NR-IQA approach based on visual codebooks. A visual codebook consisting of Gabor filter based local features extracted from local image patches is used to capture complex statistics of a natural image. The codebook encodes statistics by quantizing the feature space and accumulating histograms of patch appearances. This method does not assume any specific types of distortions, however, when evaluating images with a particular type of distortion, it does require examples with the same or similar distortion for training. Experimental results demonstrate that the predicted quality score using our method is consistent with human perceived image quality. The proposed method is comparable to state-ofthe- art general purpose NR-IQA methods and outperforms the full-reference image quality metrics, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) on the LIVE image quality assessment database.