A Novel Method For Stereo Image Quality Assessment

Kuo, Tien-Ying; Wei, Yu-Jen; Wan, Kuan-Hung; Chuang, Shao-Jung

doi:10.1109/icmew.2018.8551557

Cited by 1 publication

(4 citation statements)

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“…The quality score is the weighted average of two scores. In [24], Kuo et al obtained a reference cyclopean image and a distorted one by combining left and right images. Then, information content weighting SSIM (IW-SSIM) [46] is used to calculate the similarity between the two images.…”

Section: Related Workmentioning

confidence: 99%

“…Then, information content weighting SSIM (IW-SSIM) [46] is used to calculate the similarity between the two images. In [15], apart from cyclopean as [24], depth perception is considered by Liu et al Two depth maps and cyclopean images can be obtained from reference and distorted stereo pairs. Then, MS-SSIM [47] is used to get the similarity scores between depth maps and cyclopean images, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The quality score is the weighted average of two scores. In [24], Kuo et al. obtained a reference cyclopean image and a distorted one by combining left and right images.…”

Section: Related Workmentioning

confidence: 99%

“…According to the known reference information, objective SIQA can be classified into three types: FR, reduced-reference (RR) and no-reference (NR) approaches. FR SIQA models [15,[22][23][24][25][26][27][28][29][30] use both distorted and reference stereoscopic images to obtain visual scores. RR SIQA methods [31][32][33] only need partial information of the reference image to estimate SIQA scores.…”

Section: Related Workmentioning

confidence: 99%

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A full‐reference stereoscopic image quality assessment index based on stable aggregation of monocular and binocular visual features

Yang

Huang

et al. 2021

IET Image Processing

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In stereoscopic image quality assessment, human visual system has been universally taken into account to detect perceptual characteristics. A novel full-reference stereoscopic image assessment metric by considering both monocular and binocular visual features of human visual system is proposed. In particular, a new region segmentation algorithm is firstly proposed to divide 3D images into occluded and non-occluded regions. The just noticeable difference model is employed on the occluded regions to formulate the monocular vision, while the binocular just noticeable difference model is applied to the non-occluded regions to reveal the binocular vision of the human visual system. In the proposed region segmentation, disparity information and Euclidean distance between stereo pairs are both adopted to solve the unstable segmentation problem of traditional methods. A new pooling strategy based on global edge features is then presented to aggregate the just noticeable difference and binocular just noticeable difference evaluation maps. In addition, some local image features as supplementary of just noticeable difference to describe visual characteristics of the human visual system are also extracted. Finally, an overall quality score is calculated based on the above-mentioned features to measure the visual quality of distorted stereo pairs. Experimental results show that the proposed metric achieves high consistency with the human visual system, and outperforms state-of-the-art algorithms on stereoscopic image quality assessment.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The quality score is the weighted average of two scores. In [24], Kuo et al. obtained a reference cyclopean image and a distorted one by combining left and right images.…”

Section: Related Workmentioning

confidence: 99%