Reduced-Reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics and Structural Degradation

Ma, Jian; An, Ping; Shen, Liquan; Li, Kai

doi:10.1109/access.2017.2785282

Cited by 21 publications

(26 citation statements)

References 55 publications

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“…In order to verify the effectiveness of the SIQA model proposed in this paper, the proposed method is compared with some state-of-the-art SIQA methods, including FR-SIQA metrics (Lin et al [4], Khan and Channappayya [5], Chen et al [6], SSIM [42], Jiang et al [46]), RR-SIQA metrics (Ma et al [8]) and NR-SIQA metrics (SINQ [15], Yang et al [31], Zhou et al [32], Karimi et al [37], Yang et al [38], Fezza et al [41], BRISQUE [47]). In this paper, we utilize three commonly used criteria to quantify and verify the performance of the proposed method as well as all competing metrics.…”

Section: A Overall Performance Comparisonmentioning

confidence: 99%

“…Therefore, in order to provide a good visual experience to people, it is necessary to construct a stereo image quality assessment model consistent with the subjective score. Similarly to 2D-IQA metrics, 3D-IQA metrics can be classified into three categories according to the availability of the pristine reference image: the FR-SIQA methods [3]- [7] which use the original undistorted image as a reference to evaluate the quality of the stereopair; The RR-SIQA methods [8]- [10] which only utilize part of the pristine image when evaluating the quality of the image; and the NR-SIQA methods [11]- [17] which evaluate the quality of the image without any reference information. Compared with the first two categories, NR-SIQA method is more difficult, but it has wider application value in practical life.…”

Section: Introductionmentioning

confidence: 99%

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Blind Stereo Image Quality Assessment Based on Binocular Visual Characteristics and Depth Perception

2020

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The quality prediction of stereo images has great challenges without reference images. In this paper, we propose a novel no-reference stereo image quality assessment (NR-SIQA) model based on binocular visual characteristics and depth perception, which can effectively evaluate the quality of symmetric distortion and asymmetric distortion images. To be specific, we discriminate the different binocular behaviors by analyzing binocular visual characteristics, and construct the corresponding cyclopean view instead of single cyclopean view to simulate different binocular behaviors. Then, we extract monocular and binocular visual features from the left view, the right view and the synthetic cyclopean view. Furthermore, in order to evaluate the depth quality of the stereo image accurately, we extract the depth perception features from the weighted disparity map and the longitudinal correlation coefficient map. Finally, we construct the mapping relationship model from quality perception feature domain to quality score domain by training an adaptive enhancement algorithm based on support vector regression (SVR). We evaluate the performance of the proposed algorithm on four stereo image databases. The experimental results show that compared with the state-of-the-art full reference(FR), reduced reference(RR) and NR-SIQA algorithms, the proposed algorithm achieves highly competitive performance for both symmetric and asymmetric distortions. INDEX TERMS No reference, stereo image quality assessment, binocular visual characteristics, depth perception, longitudinal correlation coefficient map.

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Section: A Overall Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Stereo Image Quality Assessment Based on Binocular Visual Characteristics and Depth Perception

2020

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“…Actually, compared with opinion-unaware methods, more research is based on opinion-aware methods. It is common to see methods that extract the features of NSS models [34], [35]. However, different from NSS-inspired NR stereo IQA algorithms, StereoQUE [36] built the bivariate generalized Gaussian distribution (BGGD) model in order to capture the joint statistics of the luminance and the disparity subband coefficients.…”

Section: ) No Reference Siqamentioning

confidence: 99%

No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

Yang

Shen

et al. 2019

IEEE Access

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Since a large proportion of the information that is received daily is in the form of images, a highly effective no-reference stereo image quality assessment (SIQA) method is desired. This paper proposes an improved method that covers wide quality-aware features, including the structure, color, luminance, phase, and human visual system (HVS). To be specific, since human eyes are highly sensitive to the structure of images, the gradient magnitude (GM) and gradient orientation (GO) are extracted from the left and right views of the stereo image. Considering the influence of color distortions, the images are decomposed into the RGB channels to be processed, and the local gradient of the color image is obtained by adding up the RGB gradient vectors. In addition, according to the study of the two main visual channels, especially the cyclopean and disparity maps, the binocular-related images of position and phase congruency are generated. Correspondingly, two special dictionaries for the gradient and phase are trained to parse the high-dimensional sample sets. The experimental results show that the proposed metric always achieves high consistency with human subjective assessments for both symmetric and asymmetric distortions. INDEX TERMS Stereo image quality assessment (SIQA), no-reference, color image, gradient, phase.

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“…In recent years, many works [15]- [19] paid more attention in certain types of image quality assessment (IQA). Different from IQA, original images and retargeted images in IRQA were not in the same resolution.…”

Section: Related Workmentioning

confidence: 99%

A Content-Aware Image Retargeting Quality Assessment Method Using Foreground and Global Measurement

Guo

Jin

2019

IEEE Access

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Image retargeting methods aim to minimize the perceptual loss while changing sizes and aspect ratios of images. Since optimal retargeting methods for different images are generally not the same, the image retargeting quality assessment (IRQA) becomes a meaningful task. This paper proposes a content-aware image retargeting quality assessment method using foreground and global measurement to achieve better performance. In our proposed method, images are first divided into two categories according to the foreground object detection result, and then different corresponding measurements are designed for them. For those with obvious foreground object, both foreground and global measurement are applied. For others, only global measurement is conducted. Foreground measurement includes two complementary features: the high-level semantic similarity feature and the low-level size ratio feature. Global measurement includes another two features: an improved aspect ratio similarity (ARS) feature and edge group similarity (EGS) feature. Two public databases, i.e., the RetargetMe and CUHK, have been evaluated, and experimental results demonstrate that our method is quite effective, and it also provides state-of-the-art performance in the IRQA. a INDEX TERMS Image retargeting quality assessment, image quality assessment, foreground object quality measurement, semantic similarity feature.

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Reduced-Reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics and Structural Degradation

Cited by 21 publications

References 55 publications

Blind Stereo Image Quality Assessment Based on Binocular Visual Characteristics and Depth Perception

Blind Stereo Image Quality Assessment Based on Binocular Visual Characteristics and Depth Perception

No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics

A Content-Aware Image Retargeting Quality Assessment Method Using Foreground and Global Measurement

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