A statistical reduced-reference method for color image quality assessment

Although color is a fundamental feature of human visual perception, it has been largely unexplored in the reduced-reference (RR) image quality assessment (IQA) schemes. In this paper, we propose a natural scene statistic (NSS) method, which efficiently uses this information. It is based on the statistical deviation between the steerable pyramid coefficients of the reference color image and the degraded one. We propose and analyze the multivariate generalized Gaussian distribution (MGGD) to model the underlying statistics. In order to quantify the degradation, we develop and evaluate two measures based respectively on the Geodesic distance between two MGGDs and on the closed-form of the Kullback Leibler divergence. We performed an extensive evaluation of both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID 2008 benchmark and the FRTV Phase I validation process. Experimental results demonstrate the effectiveness of the proposed framework to achieve a good consistency with human visual perception. Furthermore, the best configuration is obtained with CIELAB color space associated to KLD deviation measure

Débruitage de séquences vidéo en présence de perturbations fortement impulsives

In this document, we are interested to the video denoising in the presence of heavily impulsiveperturbations. These additive perturbations which can occur during acquisition, transmission orcompression of video ows cannot be modeled in an adequate way by a Gaussian distribution.To address this problem two types of solutions are generally adopted : parametric methods andnon-parametric methods.In a first part, we propose to use the higher order statistics (HOS). The HOS-based algorithmsare compared with the techniques based on the second order statistics (SOS). The experimentalevaluation of the performances emphasizes the interest of such approach.In a second part, we propose to model the perturbation process by the α-stable distribution.The treatments resulting from this modeling show the eectiveness of the approach suggestedin term of SNR and computational time gain.Dans ce document, nous nous intéressons au débruitage de séquences vidéo en présence de perturbationsfortement impulsives. Ces perturbations additives qui peuvent être rencontrées lorsde l'acquisition, de la transmission ou compression des ux vidéo ne peuvent être modélisées defaçon adéquate par une distribution gaussienne.Pour aborder ce problème deux types de solutions sont généralement adoptées : les méthodesparamétriques et les méthodes non paramétriques.Dans une première partie, nous proposons d'utiliser des statistiques d'ordre supérieur. Les algorithmesproposés sont comparés au techniques basées sur les statistiques du second ordre.L'évaluation expérimentale des performances met en valeur l'intérêt d'une telle approche.Dans une seconde partie, nous proposons de modéliser le processus perturbateur par une loiα-stable. Les traitements issus de cette modélisation montrent l'ecacité de l'approche proposéeen terme de gain en SNR et de temps de calcul

On color image quality assessment using natural image statistics

Color distortion can introduce a significant damage in visual quality perception, however, most of existing reduced-reference quality measures are designed for grayscale images. In this paper, we consider a basic extension of well-known image-statistics based quality assessment measures to color images. In order to evaluate the impact of color information on the measures efficiency, two color spaces are investigated: RGB and CIELAB. Results of an extensive evaluation using TID 2013 benchmark demonstrates that significant improvement can be achieved for a great number of distortion type when the CIELAB color representation is used

A Reduced Reference Image Quality Measure Using Bessel K Forms Model for Tetrolet Coefficients

In this paper, we introduce a Reduced Reference Image Quality Assessment (RRIQA) measure based on the natural image statistic approach. A new adaptive transform called "Tetrolet" is applied to both reference and distorted images. To model the marginal distribution of tetrolet coefficients Bessel K Forms (BKF) density is proposed. Estimating the parameters of this distribution allows to summarize the reference image with a small amount of side information. Five distortion measures based on the BKF parameters of the original and processed image are used to predict quality scores. A comparison between these measures is presented showing a good consistency with human judgment

A Graph-based approach to derive the geodesic distance on Statistical manifolds: Application to Multimedia Information Retrieval

In this paper, we leverage the properties of non-Euclidean Geometry to define the Geodesic distance (GD) on the space of statistical manifolds. The Geodesic distance is a real and intuitive similarity measure that is a good alternative to the purely statistical and extensively used Kullback-Leibler divergence (KLD). Despite the effectiveness of the GD, a closed-form does not exist for many manifolds, since the geodesic equations are hard to solve. This explains that the major studies have been content to use numerical approximations. Nevertheless, most of those do not take account of the manifold properties, which leads to a loss of information and thus to low performances. We propose an approximation of the Geodesic distance through a graph-based method. This latter permits to well represent the structure of the statistical manifold, and respects its geometrical properties. Our main aim is to compare the graph-based approximation to the state of the art approximations. Thus, the proposed approach is evaluated for two statistical manifolds, namely the Weibull manifold and the Gamma manifold, considering the Content-Based Texture Retrieval application on different databases

Kernel-Based Laplacian Smoothing Method for 3D Mesh Denoising

International audienceIn this paper, we present an improved Laplacian smoothing technique for 3D mesh denoising. This method filters directly the vertices by updating their positions. Laplacian smoothing process is simple to implement and fast, but it tends to produce shrinking and oversmoothing effects. To remedy this problem, firstly, we introduce a kernel function in the Laplacian expression. Then, we propose to use a linear combination of denoised instances. This combination aims to reduce the number of iterations of the desired method by coupling it with a technique that leadsto oversmoothing. Experiments are conducted on synthetic triangular meshes corrupted by Gaussian noise. Results show that we outperform some existing methods in terms of objective and visual quality