No-reference image quality assessment in curvelet domain

Liu, Lixiong; Dong, Hong-Ping; Huang, Hua; Bovik, Alan C.

doi:10.1016/j.image.2014.02.004

Cited by 161 publications

(127 citation statements)

References 26 publications

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“…We compare the proposed method with a set of publicly available methods. The chosen state-ofthe-art RIQA methods are the following: Codebook Representation for No-Reference Image Assessment (CORNIA) [23], Curvlet-based Quality Assessment (CQA) [50], Spatial and Spectral Entropies Quality Assessment (SSEQ) [18], Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) [16], local ternary patterns (LTP) [20], and No-Reference Free Energy Principe Metric (NFERM) [33]. Additionally, we also compared the proposed algorithm with three well-established FR-IQA metrics, namely PSNR, structural similarity (SSIM) [32], and reducedreference image quality metric for contrast change (RIQMC) [49].…”

Section: Methodsmentioning

confidence: 99%

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Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

Freitas

Akamine

2018

J Braz Comput Soc

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In most practical multimedia applications, processes are used to manipulate the image content. These processes include compression, transmission, or restoration techniques, which often create distortions that may be visible to human subjects. The design of algorithms that can estimate the visual similarity between a distorted image and its non-distorted version, as perceived by a human viewer, can lead to significant improvements in these processes. Therefore, over the last decades, researchers have been developing quality metrics (i.e., algorithms) that estimate the quality of images in multimedia applications. These metrics can make use of either the full pristine content (full-reference metrics) or only of the distorted image (referenceless metric). This paper introduces a novel referenceless image quality assessment (RIQA) metric, which provides significant improvements when compared to other state-of-the-art methods. The proposed method combines statistics of the opposite color local variance pattern (OC-LVP) descriptor with statistics of the opposite color local salient pattern (OC-LSP) descriptor. Both OC-LVP and OC-LSP descriptors, which are proposed in this paper, are extensions of the opposite color local binary pattern (OC-LBP) operator. Statistics of these operators generate features that are mapped into subjective quality scores using a machine-learning approach. Specifically, to fit a predictive model, features are used as input to a gradient boosting machine (GBM). Results show that the proposed method is robust and accurate, outperforming other state-of-the-art RIQA methods.

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Section: Methodsmentioning

confidence: 99%

“…Also, even though some DS-IQA methods are able to predict the quality of contrast-distorted images [49], most GP-IQA methods have a poor prediction performance. This low performance leads to authors often omitting the results for these types of image distortions [18,20,23,50].…”

Section: Introductionmentioning

confidence: 99%

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

Freitas

Akamine

2018

J Braz Comput Soc

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“…In [12], Liu et al proposed an algorithm based on curvelet transform [13] [14]. The curvelet coefficient is the convolution of the curvelet with an image.…”

Section: Curveletmentioning

confidence: 99%

No-reference image quality metric based on features evaluation

Alaql

Ghazinour

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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Abstract-We develop an efficient general-purpose noreference (NR) image quality assessment (IQA) model that based on a hypothesis that an effective combination of image features can be used to develop NR-IQA approaches having competitive performance with the state-of-the-art. First, we design a collection of features, then evaluate the usefulness of each feature on different kinds of distortions using different features evaluation techniques. Therefore, we came up with a set of optimal features for each learning model in the framework. Our experimental results show that our approach outperforms state-of-the-art blind image quality prediction models.

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“…They used SVR to map the feature space to quality score of the input image. There are more algorithms for BIQA in the transform domain such as Curvelet domain 33 and Shearlet domain. 34 In another type of BIQA method, nonstatistical features (OIF) such as Gabor wavelet and gradients features are used to assess the input image quality.…”

Section: Related Workmentioning

confidence: 99%

Blind image quality assessment based on aesthetic and statistical quality-aware features

Jenadeleh

Masaeli

Moghaddam

2017

J. Electron. Imaging

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Abstract. The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

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No-reference image quality assessment in curvelet domain

Cited by 161 publications

References 26 publications

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

No-reference image quality metric based on features evaluation

Blind image quality assessment based on aesthetic and statistical quality-aware features

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