A Two-Step Framework for Constructing Blind Image Quality Indices

By Anush K. Moorthy, A. Bovik. IEEE Signal Processing Letters, 2010

1,238 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5818c076d0a12d540c55f9172dd2ed917ed43b33

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Citations in the corpus

Blind Image Quality Assessment via Deep Learning (2015, background): “BIQI [6] and LBIQ [7] first determine the distortion type of a given image and then employ an associated distortion-specific metric to predict its quality.”
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality (2011, methodology): “Here we full develop the 2-stage NSS-based IQA concept introduced in [12], by deploying a much richer set of NSS-based features that capture the dependencies between subband coefﬁcients over scales and orientations, as well as utilizing the perceptually relevant divisive normalization procedure.”
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network (2019, methodology): “For synthetic distortions, inspired by previous works [11], [18], [20], we construct a large-scale pre-training set based on the Waterloo Exploration Database [19] and PASCAL VOC 2012 [21], where the images are synthesized with nine distortion types and”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, background): “Various imaging related QC metrics have been proposed (e.g. Deshmukh and Bhosale (2010), Moorthy and Bovik (2010)) but, as they are not speciﬁc to MR or brain imaging they do not fulﬁl the needs of our study.”
Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition (2014, methodology): “Following this same principle, automatic no-reference image quality assessment (NR-IQA) algorithms try to handle the very complex and challenging problem of assessing the visual quality of images, in the absence of a reference.”
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment (2019, methodology): “These methods were BIQI [32], BLIINDS-II [35], BRISQUE [36], CORNIA [7], DI-IVINE [33], HOSA [8], and SSEQ [34].”
RankIQA: Learning from Rankings for No-Reference Image Quality Assessment (2017, methodology): “Other methods consider a range of distortions [20, 23, 18, 19].”
Weighted Guided Image Filtering (2016, methodology): “In the following section, the proposed weighting Γ G ( p (cid:48) ) in Equation (5) is used as an example to illustrate the WGIF.”
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency (2021, neutral): “Before the raise of deep learning, early version of general-purpose NR-IQA methods mainly divide into natu-ral scene statistics (NSS) based metrics [16, 17, 6, 5, 18, 19, 8, 20, 21] and learning-based metrics [22, 23, 24, 25, 26, 27].”
RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012, background): “This approach can be refined by assuming that the distorted image is subjected to a set of possible distortions known a priori.”
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features (2014, methodology): “One can see that BIQI is the fastest one with complexity O(N), where N is the total number of image pixels.”
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017, methodology): “From the model learning perspective, SVR [63], [64] is the most commonly used tool to learn f (x) for BIQA [6], [9], [10], [12], [45], [52].”
Blind image quality evaluation using perception based features (2015, methodology): “The algorithms in [6], [7], [8], [9], [10], [11], [12], [13], [14], [15] do not require such prior information about distortion types.”
MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment (2020, neutral): “[33] proposed to extract NSS features from the discrete wavelet transform (DWT) domain for blind image quality assessment.”
Subjective evaluation of stereoscopic image quality (2013, neutral): “Blind Image Quality Index (BIQI) [45]”
Rain Removal in Traffic Surveillance: Does it Matter? (2018, background): “BIQI is a no-reference algorithm for assessing the quality of an image, meaning that it does not require the rain-free image for comparison.”
Unsupervised feature learning framework for no-reference image quality assessment (2012, methodology): “Existing general-purpose NR-IQA algorithms can be broadly classified as (1) Natural scene statistics (NSS) based approaches [13, 14, 17, 18] and (2) Training-based approaches [7, 22, 29].”
End-to-End Blind Image Quality Assessment Using Deep Neural Networks (2018, methodology): “In BIQI [24], Moorthy and Bovik proposed a two-step framework for BIQA, where an image is first classified into a particular distortion category, and then the distortion-specific quality prediction is performed [24].”