Supervised dictionary learning for blind image quality assessment using quality-constraint sparse coding

Jiang, Qiuping; Shao, Feng; Jiang, Gangyi; Yu, Mei; Peng, Zongju

doi:10.1016/j.jvcir.2015.09.009

Cited by 30 publications

(8 citation statements)

References 43 publications

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“…In a word, all these methods were not attempted in terms of statistical characteristics of SCIs. The main reason is that, the artificial part in SCIs destroys the natural scene statistics (NSS) features [20], which is widely adopted for BIQA of natural images [21,22]. Hence, how to find particularly reliable statistical features, which can be used to characterize the intrinsic quality variations of SCIs, is still a difficult problem to be solved seriously and thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment of Screen Content Images via Fisher Vector Coding

Bai

Zhu

et al. 2022

IEEE Access

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This paper presents an effective blind image quality assessment (BIQA) method for screen content images (SCIs) based on Fisher vector encoding, with the hypothesis that local statistics will be altered with the change of distortions, and can be characterized by the fusion of statistical models and direction vectors. Firstly, a specific Gaussian mixture model (GMM) is generated from a corpus of pristine SCIs to simulate the local distribution of SCIs in spatial domain. Then, discriminative features are generated to characterize the quality of test image with Fisher vector coding and generated GMM. Finally, support vector regression is adopted to learn the mapping between discriminative features and subjective opinion scores. To validate the performance of our method, extensive experiments are conducted on three public SCI databases and the results well confirm its superiority over the existing relevant BIQA method of SCIs.INDEX TERMS Blind image quality assessment, screen content image, Gaussian mixture model, Fisher Vector Coding

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Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment of Screen Content Images via Fisher Vector Coding

Bai

Zhu

et al. 2022

IEEE Access

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“…Image quality evaluation methods can be classified into objective evaluation methods and subjective evaluation methods. The objective evaluation methods are categorized as full-reference (FR), 1-3 reduced-referenced, [4][5][6] and no-reference (NR), [7][8][9][10][11][12] according to whether the reference is needed. The FR metrics have high consistency with subjective evaluation results.…”

Section: Introductionmentioning

confidence: 99%

Study of image quality using event‐related potentials and eye tracking measurement

et al. 2018

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The image quality was investigated using electroencephalography and eye‐tracking measurements simultaneously. Different image qualities were obtained by changing σ values in the Gaussian kernel function. The experimental results illustrated that the measurable event‐related potentials and eye‐tracking parameters varied with the change of the image quality. The more blurred the image, the higher and earlier the P300 (event‐related potentials at latency about 300–550 ms) was elicited. Meanwhile, the visual fixation duration decreased and the detection rate increased with the increase of σ. The blurred image quality could be evaluated based on the synthetic results of the reciprocal of P300 latency and the visual fixation duration. The result showed a good correlation with the subjective rating scores, which proved the feasibility of utilizing electroencephalography and eye‐tracking technology to evaluate the blurred image quality.

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“…Theoretically, SC whose basis vectors resemble the receptive field of simple cells in the mammalian primary visual cortex (also known as the striate cortex and V1) is more suitable for obtaining the representation of images. 18 The reasons why SA encoding achieves better results compared with SC encoding in the approach of Ye et al are that SA encoding adopts the max-pooling method to extract features and SC encoding ignores the sparsity of the coefficient matrix. Inspired by overcompleteness and sparsity of SC, 19 we propose a feature extraction framework in which a sparse representation matrix of salient patches is converted to a fixed-length feature vector for NR-IQA.…”

Section: Introductionmentioning

confidence: 99%

Sparse representation of salient regions for no-reference image quality assessment

Feng

Deng

Jia

et al. 2016

International Journal of Advanced Robotic Systems

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This paper introduces an efficient feature learning framework via sparse coding for no-reference image quality assessment. The important part of the proposed framework is based on sparse feature extraction from a sparse representation matrix, which is computed using a sparse coding algorithm. Image patches extracted from salient regions of unlabeled images are used to learn a dictionary of sparse coding. The '1-norm of the sparse representation is taken as a sparse penalty term in the process of learning the dictionary and computing the sparse representation. A feature detector adopts the '1-norm together with the max-pooling results of the sparse representation matrix as the output sparse features to obtain the objective quality scores. Sparse features of salient regions are evaluated using the LIVE, CSIQ and TID2013 databases, and result in good generalization ability, performing better than or on par with other image quality assessment algorithms.

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Supervised dictionary learning for blind image quality assessment using quality-constraint sparse coding

Cited by 30 publications

References 43 publications

Blind Image Quality Assessment of Screen Content Images via Fisher Vector Coding

Blind Image Quality Assessment of Screen Content Images via Fisher Vector Coding

Study of image quality using event‐related potentials and eye tracking measurement

Sparse representation of salient regions for no-reference image quality assessment

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