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Xiaolong Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Blind noisy Image Quality Assessment using block homogeneity
    Computers & Electrical Engineering, 2014
    Co-Authors: Xiaotong Huang, Li Chen, Jing Tian, Xiaolong Zhang
    Abstract:

    Blind noisy Image Quality Assessment aims to evaluate the Quality of the degraded noisy Image without the need for the ground truth Image. To tackle this challenge, this paper proposes an Image Quality Assessment approach using block homogeneity. First, motivated by the local smoothness characteristic of the Image, a block-based homogeneity measure is proposed to estimate the statistics (e.g., variance) of the noise incurred in the Image, based on adaptively selected homogeneous Image regions. Second, an Image Quality Assessment approach is proposed by exploitie above-mentioned estimated noise variance, along with the visual masking effect of the human visual system. Experimental results are provided to demonstrate that the proposed Image noise estimation approach yields superior accuracy and stability performance to that of conventional approaches, and the proposed Image Quality Assessment approach achieves consistent performance to that of human subjective evaluation.

  • SMC - Homogeneity Based Blind Noisy Image Quality Assessment
    2013 IEEE International Conference on Systems Man and Cybernetics, 2013
    Co-Authors: Xiaotong Huang, Li Chen, Jing Tian, Xiaolong Zhang
    Abstract:

    Blind noisy Image Quality Assessment aims to evaluate the Quality of the degraded noisy Image without the need for the ground truth Image. To tackle this challenge, this paper proposes an Image Quality Assessment approach using block homogeneity. The contribution of the proposed approach is two-fold. First, a block-based homogeneity measure is proposed to estimate the statistics (e.g., variance) of the noise incurred in the Image, based on adaptively selected homogeneous Image regions. Second, an Image Quality Assessment approach is proposed by exploiting the above-mentioned estimated noise variance, along with the visual masking effect of the human visual system. Experimental results are provided to demonstrate that the proposed Image noise estimation approach yields superior accuracy and stability performance to that of conventional approaches, and the proposed Image Quality Assessment approach achieves consistent performance to that of human subjective evaluation.

C.-c. Jay Kuo - One of the best experts on this subject based on the ideXlab platform.

  • Compressed Image Quality Assessment Based on Saak Features.
    arXiv: Image and Video Processing, 2019
    Co-Authors: Xinfeng Zhang, Sam Kwong, C.-c. Jay Kuo
    Abstract:

    Compressed Image Quality Assessment plays an important role in Image services, especially in Image compression applications, which can be utilized as a guidance to optimize Image processing algorithms. In this paper, we propose an objective Image Quality Assessment algorithm to measure the Quality of compressed Images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose Images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art Image Quality Assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.

  • ICIP - Compressed Image Quality Assessment Based on Saak Features
    2019 IEEE International Conference on Image Processing (ICIP), 2019
    Co-Authors: Xinfeng Zhang, Sam Kwong, C.-c. Jay Kuo
    Abstract:

    Compressed Image Quality Assessment plays an important role in Image services, especially in Image compression applications, which can be utilized as a guidance to optimize Image processing algorithms. In this paper, we propose an objective Image Quality Assessment algorithm to measure the Quality of compressed Images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose Images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art Image Quality Assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.

  • A ParaBoost stereoscopic Image Quality Assessment (PBSIQA) system
    Journal of Visual Communication and Image Representation, 2017
    Co-Authors: Rui Song, C.-c. Jay Kuo
    Abstract:

    ParaBoost (parallel-boosting) stereoscopic Image Quality Assessment (PBSIQA) system is proposed.The system is machine-learning based 2-stage model accounting for a wide range of distortion types.Multiple scorers in stage-1 covers some targeted distortions that are complementary each other.In stage-2, multiple intermediate scores are fused to obtain boosted overall Quality score.The superiority of the proposed system is proved by extensive experimental results with several datasets. The problem of stereoscopic Image Quality Assessment, which finds applications in 3D visual content delivery such as 3DTV, is investigated in this work. Specifically, we propose a new ParaBoost (parallel-boosting) stereoscopic Image Quality Assessment (PBSIQA) system. The system consists of two stages. In the first stage, various distortions are classified into a few types, and individual Quality scorers targeting at a specific distortion type are developed. These scorers offer complementary performance in face of a database consisting of heterogeneous distortion types. In the second stage, scores from multiple Quality scorers are fused to achieve the best overall performance, where the fuser is designed based on the parallel boosting idea borrowed from machine learning. Extensive experimental results are conducted to compare the performance of the proposed PBSIQA system with those of existing stereo Image Quality Assessment (SIQA) metrics. The developed Quality metric can serve as an objective function to optimize the performance of a 3D content delivery system.

Xiaotong Huang - One of the best experts on this subject based on the ideXlab platform.

  • Blind noisy Image Quality Assessment using block homogeneity
    Computers & Electrical Engineering, 2014
    Co-Authors: Xiaotong Huang, Li Chen, Jing Tian, Xiaolong Zhang
    Abstract:

    Blind noisy Image Quality Assessment aims to evaluate the Quality of the degraded noisy Image without the need for the ground truth Image. To tackle this challenge, this paper proposes an Image Quality Assessment approach using block homogeneity. First, motivated by the local smoothness characteristic of the Image, a block-based homogeneity measure is proposed to estimate the statistics (e.g., variance) of the noise incurred in the Image, based on adaptively selected homogeneous Image regions. Second, an Image Quality Assessment approach is proposed by exploitie above-mentioned estimated noise variance, along with the visual masking effect of the human visual system. Experimental results are provided to demonstrate that the proposed Image noise estimation approach yields superior accuracy and stability performance to that of conventional approaches, and the proposed Image Quality Assessment approach achieves consistent performance to that of human subjective evaluation.

  • SMC - Homogeneity Based Blind Noisy Image Quality Assessment
    2013 IEEE International Conference on Systems Man and Cybernetics, 2013
    Co-Authors: Xiaotong Huang, Li Chen, Jing Tian, Xiaolong Zhang
    Abstract:

    Blind noisy Image Quality Assessment aims to evaluate the Quality of the degraded noisy Image without the need for the ground truth Image. To tackle this challenge, this paper proposes an Image Quality Assessment approach using block homogeneity. The contribution of the proposed approach is two-fold. First, a block-based homogeneity measure is proposed to estimate the statistics (e.g., variance) of the noise incurred in the Image, based on adaptively selected homogeneous Image regions. Second, an Image Quality Assessment approach is proposed by exploiting the above-mentioned estimated noise variance, along with the visual masking effect of the human visual system. Experimental results are provided to demonstrate that the proposed Image noise estimation approach yields superior accuracy and stability performance to that of conventional approaches, and the proposed Image Quality Assessment approach achieves consistent performance to that of human subjective evaluation.

Ling Shao - One of the best experts on this subject based on the ideXlab platform.

  • non distortion specific no reference Image Quality Assessment
    Information Sciences, 2015
    Co-Authors: Redzuan Abdul Manap, Ling Shao
    Abstract:

    Over the last two decades, there has been a surge of interest in the research of Image Quality Assessment due to its wide applicability to many domains. In general, the aim of Image Quality Assessment algorithms is to evaluate the perceptual Quality of an Image using an objective index which should be highly consistent with the human subjective index. The objective Image Quality Assessment algorithms can be classified into three main classes: full-reference, reduced-reference, and no-reference. While full-reference and reduced-reference algorithms require full information or partial information of the reference Image respectively, no reference information is required for no-reference algorithms. Consequently, a no-reference (or blind) Image Quality Assessment algorithm is highly preferred in cases where the availability of any reference information is implausible. In this paper, a survey of the recent no-reference Image Quality algorithms, specifically for non-distortion-specific cases, is provided in the first half of this paper. Two major approaches in designing the non-distortion-specific no-reference algorithms, namely natural scene statistics-based and learning-based, are studied. In the second half of this paper, their performance and limitations are discussed before current research trends addressing the limitations are presented. Finally, possible future research directions are proposed towards the end of this paper.

Bovikalan Conrad - One of the best experts on this subject based on the ideXlab platform.