Adaptive Cyclopean Image-Based Stereoscopic Image-Quality Assessment Using Ensemble Learning

Li, Sumei; Xu, Haoping; Chang, Yongli

doi:10.1109/tmm.2019.2907470

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…In [40], a blind SIQA metric by simulating the whole visual perception route from the eyes to the frontal lobe was proposed. Li et al [41] presented a FR SIQA index based on an adaptive cyclopean image by using ensemble learning. Karimi et al [42] proposed an efficient general purpose blind SIQA model based on learned features from binocular combined images.…”

Section: B Siqa Methods Developed By Simulating the Characteristics Of Hvsmentioning

confidence: 99%

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

Han

2021

IEEE Access

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Section: B Siqa Methods Developed By Simulating the Characteristics Of Hvsmentioning

confidence: 99%

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

Han

2021

IEEE Access

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“…At this time, the multiresolution model representation needs to be compressed. Li et al [30] used grids to generate regular or adaptive subdivision mechanisms, which could be used to generate models with multiple resolutions and provide an effective compression mechanism. However, they require that the input grid can meet the sub-subdivision connectivity.…”

Section: Cultural Relics Information Acquisition Technology Under DLmentioning

confidence: 99%

Digitized Visual Transformation of Grotto Art Using Deep Learning and Virtual Reality Technology

Xie

2022

Scientific Programming

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The purpose is to convert the contents of the grotto murals into a digital vision based on deep learning (DL) and virtual reality (VR) technology to protect the precious grotto murals. The grotto murals are analyzed and modeled by introducing the related concepts of the digital library, using VR technology, and taking the cultural relics data acquisition technology as the basic framework. Then, the cultural relics information collection technology under DL is combined with VR technology, the color and materialization concepts of grotto murals are integrated, and a digital recognition model of grotto mural restoration and simulation is constructed. The color analysis scheme of grotto murals is established. Through the analysis of color and material, the comprehensive recognition rate of this color model can reach 70%, and the material recognition of color painting can also ensure one-to-one restoration. Besides, the model can be applied to the simulation and restoration of grotto murals and provide some help for the protection of grotto murals. This paper has practical application value for the protection and restoration of mural art.

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“…Kang et al [12] first constructed a no reference (NR) IQA CNN, which does not require the clean image as reference. Li et al [18] exploited ensemble learning and saliency map for stereo image quality assessment. StereoQA-Net [39] presents an end-to-end dual-stream interactive network for no-reference stereo image quality assessment.…”

Section: Cnn-based Image Quality Assessmentmentioning

confidence: 99%

Perception-Oriented Stereo Image Super-Resolution

Yan

Jiang

2021

Proceedings of the 29th ACM International Conference on Multimedia

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Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of superresolved stereo images. To improve the perceptual performance, this paper proposes the first perception-oriented stereo image superresolution approach by exploiting the feedback, provided by the evaluation on the perceptual quality of StereoSR results. To provide accurate guidance for the StereoSR model, we develop the first special stereo image super-resolution quality assessment (StereoSRQA) model, and further construct a StereoSRQA database. Extensive experiments demonstrate that our StereoSR approach significantly improves the perceptual quality and enhances the reliability of stereo images for disparity estimation. CCS CONCEPTS• Computing methodologies → Reconstruction.

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Adaptive Cyclopean Image-Based Stereoscopic Image-Quality Assessment Using Ensemble Learning

Cited by 15 publications

References 26 publications

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

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

Digitized Visual Transformation of Grotto Art Using Deep Learning and Virtual Reality Technology

Perception-Oriented Stereo Image Super-Resolution

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