Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation

Ma, Jian; Xu, Guili; Han, Xiyu

doi:10.1109/access.2021.3129814

Cited by 3 publications

(1 citation statement)

References 72 publications

(109 reference statements)

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“…The quality score is calculated by calculating the difference between visual information and natural scene statistics for the original and distorted images, and then using a prediction function trained by support vector regression. Ma, Xu & Han (2021) proposed a RR-SIQA method based on gradient-based sparse representation and structural degradation. The proposed method is based on two main tasks: the first task extracts distribution statistics of visual primitives through gradient sparse representation, while the second task measures the distribution of visual primitives by extracting joint statistics of gradient magnitude and Laplacian of Gaussian features due to Structural degradation of stereoscopic images caused by distortion.…”

Section: Introductionmentioning

confidence: 99%

A full reference quality assessment method with fused monocular and binocular features for stereo images

Hu,

Bai,

Chen

et al. 2024

PeerJ Computer Science

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Aiming to automatically monitor and improve stereoscopic image and video processing systems, stereoscopic image quality assessment approaches are becoming more and more important as 3D technology gains popularity. We propose a full-reference stereoscopic image quality assessment method that incorporate monocular and binocular features based on binocular competition and binocular integration. To start, we create a three-channel RGB fused view by fusing Gabor filter bank responses and disparity maps. Then, using the monocular view and the RGB fusion view, respectively, we extract monocular and binocular features. To alter the local features in the binocular features, we simultaneously estimate the saliency of the RGB fusion image. Finally, the monocular and binocular quality scores are calculated based on the monocular and binocular features, and the quality scores of the stereo image prediction are obtained by fusion. Performance testing in the LIVE 3D IQA database Phase I and Phase II. The results of the proposed method are compared with newer methods. The experimental results show good consistency and robustness.

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Section: Introductionmentioning

confidence: 99%