Journal ArticleDOI
Objective Image Quality Assessment Based on Support Vector Regression
Manish Narwaria,Weisi Lin +1 more
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TLDR
A new approach to address the problem of objective image quality estimation, with the use of singular vectors out of singular value decomposition (SVD) as features for quantifying major structural information in images and then support vector regression (SVR) for automatic prediction of image quality.Abstract:
Objective image quality estimation is useful in many visual processing systems, and is difficult to perform in line with the human perception. The challenge lies in formulating effective features and fusing them into a single number to predict the quality score. In this brief, we propose a new approach to address the problem, with the use of singular vectors out of singular value decomposition (SVD) as features for quantifying major structural information in images and then support vector regression (SVR) for automatic prediction of image quality. The feature selection with singular vectors is novel and general for gauging structural changes in images as a good representative of visual quality variations. The use of SVR exploits the advantages of machine learning with the ability to learn complex data patterns for an effective and generalized mapping of features into a desired score, in contrast with the oft-utilized feature pooling process in the existing image quality estimators; this is to overcome the difficulty of model parameter determination for such a system to emulate the related, complex human visual system (HVS) characteristics. Experiments conducted with three independent databases confirm the effectiveness of the proposed system in predicting image quality with better alignment with the HVS's perception than the relevant existing work. The tests with untrained distortions and databases further demonstrate the robustness of the system and the importance of the feature selection.read more
Citations
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Journal ArticleDOI
No-Reference Image Quality Assessment in the Spatial Domain
TL;DR: Despite its simplicity, it is able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms.
Journal ArticleDOI
No-reference image quality assessment based on spatial and spectral entropies
TL;DR: It is found that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference IQA algorithm SSIM and several top-performing NR IQA methods: BIQI, DIIVINE, and BLIINDS-II.
Journal ArticleDOI
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data
TL;DR: A new no-reference (NR) IQA model is developed and a robust image enhancement framework is established based on quality optimization, which can well enhance natural images, low-contrast images,Low-light images, and dehazed images.
Journal ArticleDOI
Blind Image Quality Assessment Using a General Regression Neural Network
TL;DR: A no-reference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN) trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types.
Journal ArticleDOI
Perceptual image quality assessment: a survey
Guangtao Zhai,Xiongkuo Min +1 more
TL;DR: This survey provides a general overview of classical algorithms and recent progresses in the field of perceptual image quality assessment and describes the performances of the state-of-the-art quality measures for visual signals.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Selection of relevant features and examples in machine
Avrim L. Bluma,Pat Langley +1 more
TL;DR: A survey of machine learning methods for handling data sets containing large amounts of irrelevant information can be found in this article, where the authors focus on two key issues: selecting relevant features and selecting relevant examples.
Journal ArticleDOI
Selection of relevant features and examples in machine learning
Avrim Blum,Pat Langley +1 more
TL;DR: This survey reviews work in machine learning on methods for handling data sets containing large amounts of irrelevant information and describes the advances that have been made in both empirical and theoretical work in this area.
Journal ArticleDOI
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
TL;DR: The proposed VSNR metric is generally competitive with current metrics of visual fidelity; it is efficient both in terms of its low computational complexity and in termsof its low memory requirements; and it operates based on physical luminances and visual angle (rather than on digital pixel values and pixel-based dimensions) to accommodate different viewing conditions.