Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: No-reference image blur assessment using multiscale gradient | Chen and Bovik EURASIP Journal on Image and Video Processing 2011 2011 3 http content 2011 1 3 D EURASIP Journal on Image and Video Processing a SpringerOpen Journal RESEARCH Open Access No-reference image blur assessment using multiscale gradient Ming-Jun Chen and Alan C Bovik Abstract The increasing number of demanding consumer video applications as exemplified by cell phone and other lowcost digital cameras has boosted interest in no-reference objective image and video quality assessment QA algorithms. In this paper we focus on no-reference image and video blur assessment. We consider natural scenes statistics models combined with multi-resolution decomposition methods to extract reliable features for QA. The algorithm is composed of three steps. First a probabilistic support vector machine SVM is applied as a rough image quality evaluator. Then the detail image is used to refine the blur measurements. Finally the blur information is pooled to predict the blur quality of images. The algorithm is tested on the LIVE Image Quality Database and the Real Blur Image Database the results show that the algorithm has high correlation with human judgments when assessing blur distortion of images. Keywords No-reference blur metric Gradient histogram Multi-resolution analysis Information pooling 1. Introduction With the rapid and massive dissemination of digital images and videos people live in an era replete with digitized visual information. Since many of these images are of low quality effective systems for automatic image quality differentiation are needed. Although there are a variety of effective full-reference FR quality assessment QA models such as the PSNR the structural similarity SSIM index 1 2 the visual information fidelity index 3 and the visual signal-to-noise ratio VSNR 4 models for no-reference NR QA have not yet achieved performance that is competitive with top performing FR QA models. As such research in the area of blind or NR QA
