The Experts below are selected from a list of 15030 Experts worldwide ranked by ideXlab platform

Patrick Le Callet - One of the best experts on this subject based on the ideXlab platform.

  • Directional regularity for visual quality estimation
    Signal Processing, 2015
    Co-Authors: Delei Liu, Yuhui Quan, Zhiwen Yu, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the projection spatial arrangements between the reference Image and the Distorted Image. The projections of an Image are first extracted by Radon transform. Then fractal dimensions are computed on each projection and concatenated as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach was evaluated on four public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

  • Fractal Analysis for Reduced Reference Image Quality Assessment
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Y. Xu, Delei Liu, Yuhui Quan, Patrick Le Callet
    Abstract:

    In this paper, multifractal analysis is adapted to reduced-reference Image quality assessment (RR-IQA). A novel RR-QA approach is proposed, which measures the difference of spatial arrangement between the reference Image and the Distorted Image in terms of spatial regularity measured by fractal dimension. An Image is first expressed in Log-Gabor domain. Then, fractal dimensions are computed on each Log-Gabor subband and concatenated as a feature vector. Finally, the extracted features are pooled as the quality score of the Distorted Image using ℓ1 distance. Compared with existing approaches, the proposed method measures Image quality from the perspective of the spatial distribution of Image patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have demonstrated the excellent performance of the proposed method in comparison with state-of-the-art approaches.

  • Reduced reference Image quality assessment using regularity of phase congruency
    Signal Processing: Image Communication, 2014
    Co-Authors: Delei Liu, Yuhui Quan, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the phase congruency (PC) between the reference Image and the Distorted Image. The proposed model adopts a three-stage approach. The PC of the Image is first extracted, then the fractal dimensions are computed on PC as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach is evaluated on seven public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

  • Estimating the usefulness of Distorted natural Images using an Image contour degradation measure
    Journal of the Optical Society of America, 2011
    Co-Authors: David Rouse, Sheila Hemami, Romuald Pépion, Patrick Le Callet
    Abstract:

    Quality estimators aspire to quantify the perceptual resemblance, but not the usefulness, of a Distorted Image when compared to a reference natural Image. However, humans can successfully accomplish tasks (e.g., object identification) using visibly Distorted Images that are not necessarily of high quality. A suite of novel subjective experiments reveals that quality does not accurately predict utility (i.e., usefulness). Thus, even accurate quality estimators cannot accurately estimate utility. In the absence of utility estimators, leading quality estimators are assessed as both quality and utility estimators and dismantled to understand those Image characteristics that distinguish utility from quality. A newly proposed utility estimator demonstrates that a measure of contour degradation is sufficient to accurately estimate utility and is argued to be compatible with shape-based theories of object perception.

  • Method for Assessing Image Quality
    2009
    Co-Authors: Alexandre Ninassi, Olivier Le Meur, Patrick Le Callet, Dominique Barba
    Abstract:

    The invention is a method for assessing Image quality value of a Distorted Image with respect to a reference Image. The method comprises the following steps: computing, for each pixel of the Distorted Image, at least one quality level with respect to the reference Image; adding, for the Distorted Image, the quality levels associated to each pixel by weighting them by a weight depending on a perceptual interest of the pixel in order to get the Image quality value, the weight being lower for a pixel of high perceptual interest.

Delei Liu - One of the best experts on this subject based on the ideXlab platform.

  • Directional regularity for visual quality estimation
    Signal Processing, 2015
    Co-Authors: Delei Liu, Yuhui Quan, Zhiwen Yu, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the projection spatial arrangements between the reference Image and the Distorted Image. The projections of an Image are first extracted by Radon transform. Then fractal dimensions are computed on each projection and concatenated as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach was evaluated on four public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

  • Fractal Analysis for Reduced Reference Image Quality Assessment
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Y. Xu, Delei Liu, Yuhui Quan, Patrick Le Callet
    Abstract:

    In this paper, multifractal analysis is adapted to reduced-reference Image quality assessment (RR-IQA). A novel RR-QA approach is proposed, which measures the difference of spatial arrangement between the reference Image and the Distorted Image in terms of spatial regularity measured by fractal dimension. An Image is first expressed in Log-Gabor domain. Then, fractal dimensions are computed on each Log-Gabor subband and concatenated as a feature vector. Finally, the extracted features are pooled as the quality score of the Distorted Image using ℓ1 distance. Compared with existing approaches, the proposed method measures Image quality from the perspective of the spatial distribution of Image patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have demonstrated the excellent performance of the proposed method in comparison with state-of-the-art approaches.

  • Reduced reference Image quality assessment using regularity of phase congruency
    Signal Processing: Image Communication, 2014
    Co-Authors: Delei Liu, Yuhui Quan, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the phase congruency (PC) between the reference Image and the Distorted Image. The proposed model adopts a three-stage approach. The PC of the Image is first extracted, then the fractal dimensions are computed on PC as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach is evaluated on seven public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

Alan C Bovik - One of the best experts on this subject based on the ideXlab platform.

  • blind Image quality assessment from natural scene statistics to perceptual quality
    IEEE Transactions on Image Processing, 2011
    Co-Authors: Anush K Moorthy, Alan C Bovik
    Abstract:

    Our approach to blind Image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the Image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a Distorted Image without need for a reference Image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a Distorted Image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural Images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.

  • Image information and visual quality
    IEEE Transactions on Image Processing, 2006
    Co-Authors: H.r. Sheikh, Alan C Bovik
    Abstract:

    Measurement of visual quality is of fundamental importance to numerous Image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of Images or videos in a perceptually consistent manner. Image QA algorithms generally interpret Image quality as fidelity or similarity with a "reference" or "perfect" Image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the Image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of Image information to the distortion process and explore the relationship between Image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" Images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for Image QA that related Image quality with the amount of information shared between a reference and a Distorted Image. In this paper, we propose an Image information measure that quantifies the information that is present in the reference Image and how much of this reference information can be extracted from the Distorted Image. Combining these two quantities, we propose a visual information fidelity measure for Image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 Images and show that our method outperforms recent state-of-the-art Image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.

Y. Xu - One of the best experts on this subject based on the ideXlab platform.

  • Directional regularity for visual quality estimation
    Signal Processing, 2015
    Co-Authors: Delei Liu, Yuhui Quan, Zhiwen Yu, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the projection spatial arrangements between the reference Image and the Distorted Image. The projections of an Image are first extracted by Radon transform. Then fractal dimensions are computed on each projection and concatenated as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach was evaluated on four public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

  • Fractal Analysis for Reduced Reference Image Quality Assessment
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Y. Xu, Delei Liu, Yuhui Quan, Patrick Le Callet
    Abstract:

    In this paper, multifractal analysis is adapted to reduced-reference Image quality assessment (RR-IQA). A novel RR-QA approach is proposed, which measures the difference of spatial arrangement between the reference Image and the Distorted Image in terms of spatial regularity measured by fractal dimension. An Image is first expressed in Log-Gabor domain. Then, fractal dimensions are computed on each Log-Gabor subband and concatenated as a feature vector. Finally, the extracted features are pooled as the quality score of the Distorted Image using ℓ1 distance. Compared with existing approaches, the proposed method measures Image quality from the perspective of the spatial distribution of Image patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have demonstrated the excellent performance of the proposed method in comparison with state-of-the-art approaches.

  • Reduced reference Image quality assessment using regularity of phase congruency
    Signal Processing: Image Communication, 2014
    Co-Authors: Delei Liu, Yuhui Quan, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the phase congruency (PC) between the reference Image and the Distorted Image. The proposed model adopts a three-stage approach. The PC of the Image is first extracted, then the fractal dimensions are computed on PC as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach is evaluated on seven public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

Yuhui Quan - One of the best experts on this subject based on the ideXlab platform.

  • Directional regularity for visual quality estimation
    Signal Processing, 2015
    Co-Authors: Delei Liu, Yuhui Quan, Zhiwen Yu, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the projection spatial arrangements between the reference Image and the Distorted Image. The projections of an Image are first extracted by Radon transform. Then fractal dimensions are computed on each projection and concatenated as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach was evaluated on four public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.

  • Fractal Analysis for Reduced Reference Image Quality Assessment
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Y. Xu, Delei Liu, Yuhui Quan, Patrick Le Callet
    Abstract:

    In this paper, multifractal analysis is adapted to reduced-reference Image quality assessment (RR-IQA). A novel RR-QA approach is proposed, which measures the difference of spatial arrangement between the reference Image and the Distorted Image in terms of spatial regularity measured by fractal dimension. An Image is first expressed in Log-Gabor domain. Then, fractal dimensions are computed on each Log-Gabor subband and concatenated as a feature vector. Finally, the extracted features are pooled as the quality score of the Distorted Image using ℓ1 distance. Compared with existing approaches, the proposed method measures Image quality from the perspective of the spatial distribution of Image patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have demonstrated the excellent performance of the proposed method in comparison with state-of-the-art approaches.

  • Reduced reference Image quality assessment using regularity of phase congruency
    Signal Processing: Image Communication, 2014
    Co-Authors: Delei Liu, Yuhui Quan, Y. Xu, Patrick Le Callet
    Abstract:

    In this paper, a reduced-reference Image quality assessment metric is proposed, which measures the difference of the regularity of the phase congruency (PC) between the reference Image and the Distorted Image. The proposed model adopts a three-stage approach. The PC of the Image is first extracted, then the fractal dimensions are computed on PC as the Image features that characterize the Image structures from the view of the spatial distribution. Finally the Image features are pooled as the quality score using ℓ1 distance. The proposed approach is evaluated on seven public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.