No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception

Ryu, Sung‐Soo; Sohn, Kwanghoon

doi:10.1109/tcsvt.2013.2279971

Cited by 96 publications

(2 citation statements)

References 51 publications

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“…Recently, only a few works have focused on the development of RR and NR-SIQA models. Hewage et al [40] proposed an RR-SIQA algorithm, in which edges are first computed from depth maps, and then the PSNR between the reference and edge maps is used to analyze the 3D image quality. Akhter et al [41] proposed an NR-SIQA algorithm, which extracts segmented local features of artifacts from stereo-pairs and the estimated disparity map.…”

Section: Previous Work On Siqamentioning

confidence: 99%

A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information

Shen

Yang

Geng

et al. 2023

Sensors

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Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.

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Section: Previous Work On Siqamentioning

confidence: 99%

A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information

Shen

Yang

Geng

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Many binocular perception-based metrics have been proposed for improving the performance of SIQA metrics by incorporating binocular perception. Ryu and Sohn [21] proposed a blind SIQA index that measures blurriness and blockiness for the left and right views and then combines these using a binocular perception model. Shao et al [25] developed a phase-tuned quality lookup and a visual codebook from the binocular energy responses to achieve blind quality prediction by pooling.…”

Section: Blind Siqa For Singly-distorted Imagementioning

confidence: 99%

Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Shi,

Guo,

Niu

et al. 2023

Communications in Information and Systems

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Multiply-distorted stereoscopic images are common in real-world applications. The mixture of multiple distortions results in complex binocular visual behavior of multiply-distorted stereoscopic images, making it challenging for existing blind singly-distorted stereoscopic image quality assessment (IQA) methods to obtain satisfactory results on multiply-distorted stereoscopic images. Because binocular rivalry caused by different distortions in the left and right views greatly influences the final stereoscopic image quality, we propose a registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image in this paper. First, we employ a registration-based distortion representation to characterize the distortion in the stereoscopic image. Then we represent the binocular rivalry by merging the left and right views into a cyclopean image. Considering that the color and intensity of pixels in the RGB image can better reflect the information of the distorted image, then a grayscale cyclopean image is further converted to the color binocular representation using tone mapping. Finally, a multiply-distorted stereoscopic IQA method based on a double-stream convolutional neural network is proposed. The two subnetworks are used to extract quality features from the registration-based distortion representation and color binocular representation, respectively. Experimental results demonstrate that the proposed model outperforms the state-of-theart models on the multiply-distorted stereoscopic image databases.

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Stereoscopic Image Quality Assessment Based on Binocular Adding and Subtracting

Yang

Jiang

et al. 2017

Communications in Computer and Information Science

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No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception

Cited by 96 publications

References 51 publications

A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information

A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information

Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Stereoscopic Image Quality Assessment Based on Binocular Adding and Subtracting

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