Univariant assessment of the quality of images

Jung, Mathieu; Léger, D.; Gazalet, M.

doi:10.1117/1.1482096

Cited by 21 publications

(9 citation statements)

References 17 publications

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“…A large number of features are used for classification and for quality score prediction (88 features) to achieve high performance against human rates. Jung et al in 14 propose a no-reference IQA method that is based on training a neural network. The method proposed can be described in four steps.…”

Section: -12mentioning

confidence: 99%

Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images

2015

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No-reference image quality metrics are of fundamental interest as they can be embedded in practical applications. The main goal of this paper is to perform a comparative study of seven well known no-reference learning-based image quality algorithms. To test the performance of these algorithms, three public databases are used. As a first step, the trial algorithms are compared when no new learning is performed. The second step investigates how the training set influences the results. The Spearman Rank Ordered Correlation Coefficient (SROCC) is utilized to measure and compare the performance. In addition, an hypothesis test is conducted to evaluate the statistical significance of performance of each tested algorithm.

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Section: -12mentioning

confidence: 99%

Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images

2015

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“…When properly implemented, subjective methods yield accurate Objective models of image quality [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], instead, estimate perceived quality while bypassing human assessors. These models predict image quality by processing numerical quantities (''objective features'') extracted from images.…”

Section: Introductionmentioning

confidence: 99%

Objective quality assessment of displayed images by using neural networks

Gastaldo

Zunino

Heynderickx

et al. 2005

Signal Processing: Image Communication

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“…The past five years have witnessed the emergence of various new BIQA algorithms (Saad et al 2012;Moorthy and Bovik 2011;Mittal et al 2012a;Mittal et al 2012b;Mittal et al 2012c;Mittal et al 2013;He et al 2012;Ye and Doermann 2012;Moorthy and Bovik 2010;Sheikh et al 2005;Wang et al 2002;Ciancio and da Costa 2011;Gao et al 2009;Brandao and Queluz 2008;Jung et al 2002;Charrier et al 2006). These BIQA metrics can be broadly divided into two categories: the distortion-specific metrics and the universal metrics.…”

Section: Related Workmentioning

confidence: 99%

“…Until now, most BIQA metrics available try to extract statistical features based on the natural scene statistics (NSS) (Brandao and Queluz 2008) and learn the mapping function from the features to the quality score using the supervised learning technique based on a large amount of labeled images (Moorthy and Bovik 2010;Jung et al 2002;Charrier et al 2006). Although several promising BIQA metrics have been proposed based on this methodology, there are two drawbacks of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Blind image quality assessment via semi-supervised learning and fuzzy inference

Mei

Gao

et al. 2015

Appl Inform

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Blind image quality assessment (BIQA) is a challenging task due to the difficulties in extracting quality-aware features and modeling the relationship between the image features and the visual quality. Until now, most BIQA metrics available try to extract statistical features based on the natural scene statistics (NSS) and build mapping from the features to the quality score using the supervised machine learning technique based on a large amount of labeled images. Although several promising metrics have been proposed based on the above methodology, there are two drawbacks of these algorithms. First, only the labeled images are adopted for machine learning. However, it has been proved that using unlabeled data in the training stage can improve the learning performance. In addition, these metrics try to learn a direct mapping from the features to the quality score. However, subjective quality evaluation would be rather a fuzzy process than a distinctive one. Equally, human beings tend to evaluate the quality of a given image by first judging the extents it belongs to "excellent," "good," "fair," "bad," and "poor," and estimating the quality score subsequently, rather than directly giving an exact subjective quality score. To overcome the aforementioned problems, we propose a semi-supervised and fuzzy framework for blind image quality assessment, S 2 F 2 , in this paper. In the proposed framework, (1) we formulate the fuzzy process of subjective quality assessment by using fuzzy inference. Specially, we model the membership relation between the subjective quality score and the truth values it belongs to "excellent," "good," "fair," "bad," and "poor" using a Gaussian function, respectively; and (2) we introduce the semi-supervised local linear embedding (SS-LLE) to learn the mapping function from the image features to the truth values using both the labeled and unlabeled images. In addition, we extract image features based on NSS since it has led to promising performances for image quality assessment. Experimental results on two benchmarking databases, i.e., the LIVE database II and the TID2008 database, demonstrate the effectiveness and promising performance of the proposed S 2 F 2 algorithm for BIQA.

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Univariant assessment of the quality of images

Cited by 21 publications

References 17 publications

Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images

Comparison of no-reference image quality assessment machine learning-based algorithms on compressed images

Objective quality assessment of displayed images by using neural networks

Blind image quality assessment via semi-supervised learning and fuzzy inference

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