Assessment of image quality based on structural information is a relevant topic in the image processing field. A new method to assess image quality is presented. It is based on the comparison of first and second order moments of the local variance distribution of two images. This new quality index is better suited to assess the non-stationarity of images, therefore it explicitly focuses on the ...

In this paper, we present a novel method of no-reference image quality assessment (NR-IQA), which is to predict the perceptual quality score of a given image without using any reference image. The proposed method harnesses three functions (i) the visual attention mechanism, which affects many aspects of visual perception including image quality assessment, however, is overlooked in the NR-IQA l...

In image processing, efficiency term refers to the ability in capturing significant information that is sensitive to human visual system with small description. Natural images or scenes that contain intrinsic geometrical structures (contours) are key features of visual information. The existing transform methods like Fourier transformation, wavelets, curvelets, ridgelets etc., have limitations ...

Traditionally, image quality assessment has involved the comparison of a corrupted image with an “original” or perfect version of that given image. In many practical settings, this perfect image is not available. This research introduces a new metric that measures the perceived visual quality of a single given image. Operating in this no-reference framework, the new method is ideally suited for...

Image quality assessment is a complex problem due to subjective nature of human visual perception. One of possible ways to take into consideration user’s subjectivism is to develop interactive system which could learn the properties of user’s visual perception. In this paper we present a novel way to do this via interactive neuroevolution approach. The key feature of this approach is training o...

SIFT (Scale Invariant Feature Transform) points are scale-space extreme points, representing local minutiae structure features in the Gaussian scale space. SIFT intensity, as a novel no-reference metric, is feasible to assess various common distortions without the access to reference images. The metric introduces image preprocessing: neighborhood enhancement through contrast enhancement of adja...

An innovative way of calculating the von Mises distribution of image entropy is introduced in this paper. The von Mises distribution's concentration parameter and some fitness parameter that will be defined later have been analyzed in the experimental part for determining their suitability as an image quality assessment measure in some particular distortions such as Gaussian blur or additive Ga...

In this paper, we propose to learn the structures of stereoscopic image based on convolutional neural network (CNN) for no-reference quality assessment. Taking image patches from the stereoscopic images as inputs, the proposed CNN can learn the local structures which are sensitive to human perception and representative for perceptual quality evaluation. By stacking multiple convolution and max-...

We propose a family of image quality assessment (IQA) models based on natural scene statistics (NSS), that can predict the subjective quality of a distorted image without reference to a corresponding distortionless image, and without any training results on human opinion scores of distorted images. These ‘completely blind’ models compete well with standard non-blind image quality indices in ter...

--Image Quality assessment plays an important role in various image processing applications. It is still an active area of research. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate well with perceived human quality measurement or subjective methods. Most full reference(FR) technique were derived based on pixel to pixel error such as...