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 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

No-reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics

We present two contributions in this work: (i) a bivariate generalized Gaussian distribution (BGGD) model for the joint distribution of luminance and disparity subband coefficients of natural stereoscopic scenes and (ii) a no-reference (NR) stereo image quality assessment algorithm based on the BGGD model. We first empirically show that a BGGD accurately models the joint distribution of luminance and disparity subband coefficients. We then show that the model parameters form good discriminatory features for NR quality assessment. Additionally, we rely on the previously established result that luminance and disparity subband coefficients of natural stereo scenes are correlated, and show that correlation also forms a good feature for NR quality assessment. These features are computed for both the left and right luminance-disparity pairs in the stereo image and consolidated into one feature vector per stereo pair. This feature set and the stereo pair׳s difference mean opinion score (DMOS) (labels) are used for supervised learning with a support vector machine (SVM). Support vector regression is used to estimate the perceptual quality of a test stereo image pair. The performance of the algorithm is evaluated over popular databases and shown to be competitive with the state-of-the-art no-reference quality assessment algorithms. Further, the strength of the proposed algorithm is demonstrated by its consistently good performance over both symmetric and asymmetric distortion types. Our algorithm is called Stereo QUality Evaluator (StereoQUE)

Image quality and forgery detection copula-based algorithms

Copula functions are important tools to investigate dependence structure between random variables. There are many copulas such as: Gaussian, Marshall-Olkin, Clayton, and Frank copulas. Although, copulas have been used in finance, oceanography, and hydrology, they have been applied in limited applications in the image processing field. In this thesis, copulas are applied to calculate the mutual information of two images, which in turn is used to measure image quality of a targeted image and also used to detect copy-move forgery in images. The proposed algorithms introduce new alternatives for existing image quality assessment and forgery detection methods. These algorithms are easy to use and highly accurate. The results for our image quality assessment algorithm are comparable or better than those of established methods in the literature, while the results for our image forgery detection algorithm are accurate even after applying different manipulation and post-processing techniques on the forged images. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b214099

Natural Image Statistics for Natural Image Segmentation

Building on recent progress in modeling filter response statistics of natural mages we integrate a statistical model into a variational framework for image segmentation. Incorporated in asound probabilistic distance measure the model drives level sets toward meaningful segment at ions of complex textures and natural scenes. Despite its enhanced descriptive power our approach preserves the efficiency of level set based segmentation since each connected region comprises two model parameters only. We validate the statistical basis of our model on thousands of natural images and demonstrate that our approach outperforms recent variational segment at ion methods based on second-order statistics