Blind image quality assessment with improved natural scene statistics model

Zhang, Yazhong; Wu, Jinjian; Xie, Xuemei; Li, Leida; Shi, Guangming

doi:10.1016/j.dsp.2016.05.012

Cited by 35 publications

(13 citation statements)

References 35 publications

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“…The proposed methodology is compared with 11 state-of-the-art NR-IQA techniques, namely DIIVINE [2], BLINDS II [25], BRISQUE [27], BIQI [24], CORNIA [26], M3 [1], improved NSS [18], SA _ IQA [29], S [31], and SLKR [31]. Table 2 shows the individual performance comparison of the proposed methodology for the LIVE, TID2008, and CSIQ databases, which validates the better performance of the proposed method.…”

Section: Performance Comparisonmentioning

confidence: 62%

“…The proposed method also reduces the execution time by more than 25.2% when compared to DIIVINE. The proposed method takes a longer time to compute the quality score than BIQI [24], CORNIA [26], BRISQUE [27], M3 [1], curveletQA [3], SSEQ [4], improved NSS [18], SA _ IQA [29], S [31], and SLKR [31], but gives better performance in terms of average SROCC and LCC to predict the MOS.…”

Section: Performance Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Nizami¹,

Majid²,

Khurshid³

2018

Turk J Elec Eng & Comp Sci

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Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user's quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identification based image verity and integration evaluation that selects distinct feature groups for each distortion type. The selection procedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score. Only those feature groups are used in the prediction model that have majority features with SROCC score greater than mean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performance in comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on three commonly used databases.

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Section: Performance Comparisonmentioning

confidence: 62%

Section: Performance Comparisonmentioning

confidence: 99%

Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Nizami¹,

Majid²,

Khurshid³

2018

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Most BIQA techniques extract features, which are altered in the presence of distortion [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Features are extracted either in spatial domain, wavelet domain, and discrete cosine transform (DCT) domain or using edge information of image.…”

Section: Introductionmentioning

confidence: 99%

Distortion-specific feature selection algorithm for universal blind image quality assessment

Nizami

Majid

Manzoor

et al. 2019

J Image Video Proc.

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Blind image quality assessment (BIQA) aims to use objective measures for predicting the quality score of distorted images without any prior information regarding the reference image. Several BIQA techniques are proposed in literature that use a two-step approach, i.e., feature extraction for distortion classification and regression for predicting the quality score. In this paper, a three-step approach is proposed that aims to improve the performance of BIQA techniques. In the first step, feature extraction is performed using existing BIQA techniques to determine the distortion type. Secondly, features are selected for each distortion type based on the mean value of Spearman rank ordered correlation constant (SROCC) and linear correlation constant (LCC). Lastly, distortion-specific features are used by regression model to predict the quality score. Experimental results show that the predicted quality score using distortion-specific features strongly correlates with the subjective quality score, improves the overall performance of existing BIQA techniques, and reduces the processing time.

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“…Most existing works focus on the measuring stage, where quality-aware features are designed to measure the level of image distortion. These features are usually based on the natural scene statistics (NSS) [32][33][34][35], assuming that pristine natural images have particular statistical properties that are disturbed by the distortions. NSS-based methods can extract features in different domains, such as discrete cosine transform (DCT) domain [36][37][38], discrete wavelet transform (DWT) domain [39][40][41], spatial domain [42], etc.…”

Section: Introductionmentioning

confidence: 99%

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

et al. 2018

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Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.The filters W i are learned via independent component analysis (ICA). The binarized features b i are aggregated following the same procedure described for generating the LBP labels. The descriptive features are obtained by computing the histogram of the aggregated data.Similarly to the LBP, which generates LBP channels, the BSIF generates coded images. These coded images are the set of labels generated after the binarized features are computed using Equation (17) and aggregated using Equation (1). The aggregation of BSIF results is based on a selected number of bits, instead of the number of neighbors of the labeled pixel. The labeling depends on the relationship between the patch size l and the number of binarized features b i . Figure 6 shows the BSIF coded images corresponding to the same reference using different BSIF parameters. As can be seen in this figure, the textured information depends on the patch size l and on the number of bits. The number of bits is less or equal l 2 − 1. This is the reason why the second column does not contain BSIF coded images for 9, 10, 11, or 12 bits. Figure 6 shows that the choice of the number of bits and patch sizes is important for texture analysis algorithms. Therefore, multiscale approaches that incorporate several combinations of these parameters are interesting [74][75][76][77].

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Blind image quality assessment with improved natural scene statistics model

Cited by 35 publications

References 35 publications

Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Distortion-specific feature selection algorithm for universal blind image quality assessment

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

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