The Experts below are selected from a list of 84393 Experts worldwide ranked by ideXlab platform
Cheng Zhuo - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Jianing Deng, Li Wang, Cheng ZhuoAbstract:Recent years have witnessed remarkable success of deep learning methods in Quality Enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective Quality Enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video Quality Enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video Quality Enhancement in terms of both accuracy and efficiency.
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AAAI - Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement.
2020Co-Authors: Jianing Deng, Li Wang, Cheng ZhuoAbstract:Recent years have witnessed remarkable success of deep learning methods in Quality Enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective Quality Enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video Quality Enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video Quality Enhancement in terms of both accuracy and efficiency.
Jianing Deng - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Jianing Deng, Li Wang, Cheng ZhuoAbstract:Recent years have witnessed remarkable success of deep learning methods in Quality Enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective Quality Enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video Quality Enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video Quality Enhancement in terms of both accuracy and efficiency.
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AAAI - Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement.
2020Co-Authors: Jianing Deng, Li Wang, Cheng ZhuoAbstract:Recent years have witnessed remarkable success of deep learning methods in Quality Enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective Quality Enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video Quality Enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video Quality Enhancement in terms of both accuracy and efficiency.
Zhang Zhi-xiong - One of the best experts on this subject based on the ideXlab platform.
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Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Wei Fan, Francois Cayre, Kai Wang, Zhang Zhi-xiongAbstract:Median filtering enjoys its popularity as a widely adopted image denoising and smoothing tool. It is also used by anti-forensic researchers in helping disguise traces of other image processing operations, e.g., image resampling and JPEG compression. This paper proposes an image variational deconvolution framework for both Quality Enhancement and anti-forensics of median filtered (MF) images. The proposed optimization-based framework consists of a convolution term, a fidelity term with respect to the MF image, and a prior term. The first term is for the approximation of the median filtering process, using a convolution kernel. The second fidelity term keeps the processed image to some extent still close to the MF image, retaining some denoising or other image processing artifact hiding effects. Using the generalized Gaussian as the distribution model, the last image prior term regularizes the pixel value derivative of the obtained image so that its distribution resembles the original one. Our method can serve as an MF image Quality Enhancement technique, whose efficacy is validated by experiments conducted on MF images which have been previously “salt & pepper” noised. Using another parameter setting and with an additional pixel value perturbation procedure, the proposed method outperforms the state-of-the-art median filtering anti-forensics, with a better forensic undetectability against existing detectors as well as a higher visual Quality of the processed image. Furthermore, the feasibility of concealing image resampling traces and JPEG blocking artifacts is demonstrated by experiments, using the proposed median filtering anti-forensic method.
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Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Wei Fan, Francois Cayre, Kai Wang, Zhang Zhi-xiongAbstract:Median filtering enjoys its popularity as a widely adopted image denoising and smoothing tool. It is also used by anti-forensic researchers in helping disguise traces of other image processing operations, e.g., image resampling and JPEG compression. This paper proposes an image variational deconvolution framework for both Quality Enhancement and anti-forensics of median filtered (MF) images. The proposed optimization-based framework consists of a convolution term, a fidelity term with respect to the MF image, and a prior term. The first term is for the approximation of the median filtering process, using a convolution kernel. The second fidelity term keeps the processed image to some extent still close to the MF image, retaining some denoising or other image processing artifact hiding effects. Using the generalized Gaussian as the distribution model, the last image prior term regularizes the pixel value derivative of the obtained image so that its distribution resembles the original one. Our method can serve as an MF image Quality Enhancement technique, whose efficacy is validated by experiments conducted on MF images which have been previously “salt & pepper” noised. Using another parameter setting and with an additional pixel value perturbation procedure, the proposed method outperforms the state-of-the-art median filtering anti-forensics, with a better forensic undetectability against existing detectors as well as a higher visual Quality of the processed image. Furthermore, the feasibility of concealing image resampling traces and JPEG blocking artifacts is demonstrated by experiments, using the proposed median filtering anti-forensic method.
Li Wang - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Jianing Deng, Li Wang, Cheng ZhuoAbstract:Recent years have witnessed remarkable success of deep learning methods in Quality Enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective Quality Enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video Quality Enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video Quality Enhancement in terms of both accuracy and efficiency.
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AAAI - Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement.
2020Co-Authors: Jianing Deng, Li Wang, Cheng ZhuoAbstract:Recent years have witnessed remarkable success of deep learning methods in Quality Enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective Quality Enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video Quality Enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video Quality Enhancement in terms of both accuracy and efficiency.
Noha Elassy - One of the best experts on this subject based on the ideXlab platform.
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The concepts of Quality, Quality assurance and Quality Enhancement
Quality Assurance in Education, 2015Co-Authors: Noha ElassyAbstract:Purpose – This paper aims to critically review and discuss different definitions of the concepts of Quality, Quality assurance (QA) and Quality Enhancement (QE) in higher education (HE) with presenting critical perspectives of the literature. Design/methodology/approach – The paper looks at literature concerns with the meaning of Quality, QA and QE, regarding HE context. It analysis and critically reviews the different definitions of these key concepts. Findings – This paper suggests that the concepts of QA and QE should be dealt as part of a continuum and showed the need for both as an ongoing process in HE institutions. Originality/value – The paper provides a unique analysis of the widely cited pieces of research regarding the concept of Quality, QA and QE. It contributes to increase the understanding of those key concepts in HE sector, its origin and mean stream view. It outlines the importance of having a clear understanding of these terms and highlights the difficulties of having a unified definition.
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The concepts of Quality, Quality assurance and Quality Enhancement
Quality Assurance in Education, 2015Co-Authors: Noha ElassyAbstract:Purpose – This paper aims to critically review and discuss different definitions of the concepts of Quality, Quality assurance (QA) and Quality Enhancement (QE) in higher education (HE) with presenting critical perspectives of the literature. Design/methodology/approach – The paper looks at literature concerns with the meaning of Quality, QA and QE, regarding HE context. It analysis and critically reviews the different definitions of these key concepts. Findings – This paper suggests that the concepts of QA and QE should be dealt as part of a continuum and showed the need for both as an ongoing process in HE institutions. Originality/value – The paper provides a unique analysis of the widely cited pieces of research regarding the concept of Quality, QA and QE. It contributes to increase the understanding of those key concepts in HE sector, its origin and mean stream view. It outlines the importance of having a clear understanding of these terms and highlights the difficulties of having a unified definition.
