Study of Subjective Quality and Objective Blind Quality Prediction of Stereoscopic Videos

Appina, Balasubramanyam; Dendi, Sathya Veera Reddy; Manasa, K; Channappayya, Sumohana S.; Bovik, Alan C.

doi:10.1109/tip.2019.2914950

Cited by 40 publications

(44 citation statements)

References 61 publications

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“…Our proposed model was validated on three publicly available stereoscopic video quality databases: the NAMA3DS1-COSPAD1 database [38] , the SVQA database [8] [49], the 3D IQA algorithm SINQ [28], the 2D VQA algorithms ViS3 [51] and SpEED [50], and the 3D VQA algorithms SJND [8], MNSVQM [9], Modi3d [52], Wang's model [14] and BSVQE [7]. The IQA methods were applied on each video frames and used mean pooling to generate a final score, while the VQA methods were run using the settings reported in the original paper.…”

Section: Resultsmentioning

confidence: 99%

“…The IQA methods were applied on each video frames and used mean pooling to generate a final score, while the VQA methods were run using the settings reported in the original paper. The software release of each of the selected algorithms except SJND [8], MNSVQM [9], Modi3d [52] and Wang's model [14] is publicly available.…”

Section: Resultsmentioning

confidence: 99%

“…Compared with BSVQE algorithm, SVIPOS still achieved competitive performance on the Waterloo IVC Phase I database. This may be because the cyclopean and product images computed on the left and right videos we use are more effective for binocular perception than simple summation or difference of left and right videos, and our method used the AGGD model when computing NSS features, which may be partially suitable for representing information in synthetic stereoscopic videos of unnatural scenes [52] from the Waterloo IVC Phase I database. These results show that SVIPOS performed best in 10 of 12 performance comparison metrics.…”

Section: A Performance On Three Databasesmentioning

confidence: 99%

“…To verify the statistical significance of the performance of all the compared models except SJND [8], MNSVQM [9], Modi3d [52] and Wang's model [14], we implemented the statistical tests [53] on the NAMA3DS1-COSPAD1, SVQA and Waterloo IVC Phase I databases. Fig.…”

Section: Statistical Significancementioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: A Performance On Three Databasesmentioning

confidence: 99%

Section: Statistical Significancementioning

confidence: 99%

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Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

et al. 2020

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“…ere are mainly two groups of methods on 3D image quality assessment (IQA): subjective quality evaluation by human observer [4] and objective quality evaluation by devised metric used to simulate human perceptual judgements [5]. Since the human eyes are the final receiver of visual information, the subjective evaluation can directly reflect the human visual perception and is accurate and effective to evaluate the visual quality.…”

Section: Introductionmentioning

confidence: 99%

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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Research on robotic manipulator fault detection and diagnosis technology based on machine vision in complex environments

Pan

Peng

2022

Journal of Field Robotics

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Currently, machine vision-based fault detection and diagnosis technology for robotic manipulators is widely used. However, traditional machine vision has difficulty identifying manipulator failure in complex environments with dim lighting and large texture differences and the influence of such factors as image motion blur caused by robotic manipulator movement. This article discusses the failure factors of mechanical manipulators and systematically analyzes various links leading to failure and the current technology limitations. First, a gradient-based semantic segmentation method is proposed to extract targets quickly and accurately for the grasped object and complex surrounding environment. Second, when the vision and grasped object have relative movement in dim environment, a multiframe image registration and fusion method are proposed to obtain high-quality, clear image data. Then, a machine-based method is adopted to learn the fault detection and diagnosis methods that fuse internal and external sensors. Finally, a physical system is built to verify the three aspects of the target extraction effect: image clarity, fault detection speed, and diagnosis accuracy, reflecting the superiority of this algorithm.

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Study of Subjective Quality and Objective Blind Quality Prediction of Stereoscopic Videos

Cited by 40 publications

References 61 publications

Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

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

Research on robotic manipulator fault detection and diagnosis technology based on machine vision in complex environments

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