No-Reference Stereoscopic Image Quality Assessment Based on Image Distortion and Stereo Perceptual Information

Shen, Liquan; Fang, Ruigang; Yang, Yanxi; Geng, Xianqiu; Wu, Dapeng

doi:10.1109/tetci.2018.2804885

Cited by 34 publications

(12 citation statements)

References 46 publications

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“…In each case, the results of the best-performance metric are marked in bold. According to the experimental results in this table, Shen et al's method [22] performs best on asymmetrically distorted stereoscopic images in the LIVE 3D IQA database Phase II, and our proposed scheme achieves higher consistency with human opinion scores on the other two databases. Moreover, the PLCC and SRCC values for our scheme are above 0.912 and 0.907, respectively, on all databases, which demonstrate that the proposed scheme exhibits a good stability to quantify and predict the perceptual distortions of 3D images.…”

Section: Overall Performance Comparisonmentioning

confidence: 85%

“…ese mainly include two 2D-IQA methods (PSNR and multiscale structural similarity (MS-SSIM) [55]), two FR 3D-IQA methods (Benoit et al's method [13] and Chen et al's method [8]), and three NR 3D-IQA methods (Zhou and Yu's method [56], Fan et al's method [41], and Shen et al's method [22]). For the previous two 2D-IQA approaches, the predicted quality score of a stereoscopic image was obtained by averaging the left and right image qualities.…”

Section: Overall Performance Comparisonmentioning

confidence: 99%

“…From these tables, we can find that our proposed metric achieves the highest hit-count for each index and is statistically superior to the compared methods. Some metrics may have high assessment accuracies for specific distortion types: Chen et al's method [8] shows strong competitiveness on Gaussian blur, and Shen et al's method [22] has outstanding performance on JPEG compression . But our method is comparable to the best-performing metrics for these kinds of distortions.…”

Section: Distortion-specific Performance Evaluationmentioning

confidence: 99%

“…e scores of perceptual blockiness and blurriness were combined into an overall quality index based on the binocular perception models. Shen et al [22] devised a no-reference quality scheme for stereoscopic images based on the visual perceptual characteristics.…”

Section: Introductionmentioning

confidence: 99%

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No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Guo²,

Chen

et al. 2021

Complexity

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Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

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Section: Overall Performance Comparisonmentioning

confidence: 85%

Section: Overall Performance Comparisonmentioning

confidence: 99%

Section: Distortion-specific Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Guo²,

Chen

et al. 2021

Complexity

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“…Since a cyclopean image can reflect the comprehensive stimulus and binocular perception of left and right views, it is often used to calculate the perceived quality of three-dimensional scene [15]. Shen et al [25] proposed an NR 3D IQA method, which uses a new disparity search algorithm to compute a cyclopean image. Liu et al [26] computed a cyclopean image by utilizing a 3D saliency map to weight the left and right views.…”

Section: Introductionmentioning

confidence: 99%

Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

et al. 2020

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We develop a new no-reference (NR) stereoscopic video quality assessment model that adopts oriented local gravitational force (OLGF) statistics in the space-time domain. The OLGF is a novel extension of an existing local gravitational force descriptor and includes two new components: relative local gravitational force magnitude and relative local gravitational force orientation. The resulting algorithm, called Stereoscopic Video Integrity Predictor using OLGF Statistics (SVIPOS), first uses our previous work to generate a cyclopean image and a product image in spatial domain from a stereoscopic video sequence pair, and calculates a frame difference image in temporal domain from a left video sequence. Then the OLGF model is deployed to compute various local gravitational force responses on the cyclopean image, the product image and the frame difference image. Finally, a support vector regression is used to map statistical features extracted from the computed gravitational force responses to stereoscopic video quality predications. SVIPOS is thoroughly tested on three publicly stereoscopic video quality databases. The experimental results show that SVIPOS outperforms state-of-the-art stereoscopic video quality methods. INDEX TERMS Stereoscopic video quality assessment, oriented local gravitational force, binocular perception, natural scene statistics.

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Contrastive distortion‐level learning‐based no‐reference image‐quality assessment

Wei

Zhou

et al. 2022

Int J of Intelligent Sys

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A contrastive distortion-level learning-based no-reference image-quality assessment (NR-IQA) framework is proposed in this study to further effectively model various distortion types with the same or different distortion levels.The proposed method aims to improve the prediction accuracy of NR-IQA. The proposed method consists of three parts: multiscale distortion-level representation learning, single-image NR-IQA, and a representation affinity module, which can reduce NR-IQA computational complexity while maintaining a low-distortion representation of high-distortion inputs. The proposed NR-IQA method aims to extract distributional features of samples in real distorted images and predict ambiguity based on distortion-level learning. Experimental results show that by comparing on many NR-IQA data sets the proposed method can outperform state-of-the-art methods.

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No-Reference Stereoscopic Image Quality Assessment Based on Image Distortion and Stereo Perceptual Information

Cited by 34 publications

References 46 publications

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

Contrastive distortion‐level learning‐based no‐reference image‐quality assessment

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