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...
—The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border control applications has heightened the need for ensuring that the...
We study the problem of automatic “reduced reference” image quality assessment algorithms from the point of view of image information change. Algorithms that measure differenc...
Most present day no-reference/blind image quality assessment (NR IQA) algorithms are distortion specific - i.e., they assume that the distortion affecting the image is known. Here...
The aim of an objective image quality assessment is to find an automatic algorithm that evaluates the quality of pictures or video as a human observer would do. To reach this goal...
Alexandre Ninassi, Olivier Le Meur, Patrick Le Cal...
In this paper, a novel learning based method is proposed for No-Reference image quality assessment. Instead of examining the exact prior knowledge for the given type of distortion...
Computational representation of perceived image quality is a fundamental problem in computer vision and image processing, which has assumed increased importance with the growing r...
Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that i...
The Structural SIMilarity (SSIM) index is a novel method for measuring the similarity between two images. The SSIM index can be viewed as a quality measure of one of the images bei...