No-reference image quality assessment based on spatial and spectral entropies

Liu, Lixiong; Bao, Lixia; Huang, Hua; Bovik, Alan C.

doi:10.1016/j.image.2014.06.006

Cited by 587 publications

(357 citation statements)

References 23 publications

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“…We train metrics on one database and test them on the other databases. Four methods are employed to compare: BIQI [1], BRISQUE [17], SSEQ [11], BLIINDS-II [15].…”

Section: Experiments and Resultsmentioning

confidence: 99%

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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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“…We train metrics on one database and test them on the other databases. Four methods are employed to compare: BIQI [1], BRISQUE [17], SSEQ [11], BLIINDS-II [15].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In [11], Liu et al presented the SSEQ algorithm which estimates quality based on image entropy since the type and the amount of the distortion affect the local entropy of images. The features are computed from the spatial entropy and the spectral entropy.…”

Section: B Sseqmentioning

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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“…In the state-of-the-art methods that we employed to enrich, the authors used SVR as the regression machine. 7,9,10,11,74 We used the LIBSVM package 75 implementation of SVR with the suggested parameters from each method.…”

Section: Enriching Biqa Methodsmentioning

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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“…The LIVE image database [5] was used for performance evaluation of the model. In [10], authors developed an efficient general-purpose NR image quality assessment model that utilizes local spatial and spectral entropy features on distorted images, tested on LIVE image database [5] and checked on TID2008 image database [11] (among five degradation types from LIVE dataset). In [12], authors presented a novel metric for image quality assessment of blurred images.…”

Section: Related Workmentioning

confidence: 99%