68,011 research outputs found

    A statistical reduced-reference method for color image quality assessment

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    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

    Quantifying appearance retention in carpets using geometrical local binary patterns

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    Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations has shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique performs better to distinguish consecutive AR grades in carpets

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

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    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

    On color image quality assessment using natural image statistics

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    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

    Feature extraction of the wear label of carpets by using a novel 3D scanner

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    In the textile industry, the quality of carpets is still determined through visual assessment by human experts. Human assessment is somewhat subjective, so there is a need for a more objective assessment which yields to automated systems. However, existing computer models are at this moment not yet capable of matching the human expertise. Most attempts at automated assessment have focused on image analysis of two dimensional images of worn carpet. These do not adequately capture the three dimensional structure of the carpet that is also evaluated by the experts and the image processing is very dependent on the lighting conditions. One previous attempt however used a laser scanner to obtain three dimensional images of the carpet and process them for carpet assessment. This paper describes the development of a new scanner to acquire wear label characteristics in three dimensions based on a structured light pattern. Now an appropriate technique based on the local binary patterns (LBP) and the Kullback-Leibler divergence has been developed. We show that the new laser scanning system is less dependent on the lighting conditions and color of the carpet and obtains data points on a structured grid instead of sparse points. The new system is also more than five times cheaper, scans more than seven times faster and is specifically designed for scanning carpets instead of 3D objects. Previous attempts to classify the carpet wear were based on several extracted features. Only one of them - the height difference between worn and unworn part - showed a good correlation of 0.70 with the carpet wear label. However, experiments demonstrate that our approach - using the LBP technique - gives rise to promising results, with correlation factors from 0.89 to 0.99 between the Kullback-Leibler divergence and quality labels. This new laser scanner system is a significant step forward in the automated assessment of carpet wear using 3D images

    Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

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    Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
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