The Experts below are selected from a list of 2052 Experts worldwide ranked by ideXlab platform

Ruiqin Xiong - One of the best experts on this subject based on the ideXlab platform.

  • graph based non convex low rank regularization for image Compression Artifact reduction
    IEEE Transactions on Image Processing, 2020
    Co-Authors: Ruiqin Xiong, Dong Liu, Xiaopeng Fan, Wen Gao
    Abstract:

    Block transform coded images usually suffer from annoying Artifacts at low bit-rates, because of the independent quantization of DCT coefficients. Image prior models play an important role in compressed image reconstruction. Natural image patches in a small neighborhood of the high-dimensional image space usually exhibit an underlying sub-manifold structure. To model the distribution of signal, we extract sub-manifold structure as prior knowledge. We utilize graph Laplacian regularization to characterize the sub-manifold structure at patch level. And similar patches are exploited as samples to estimate distribution of a particular patch. Instead of using Euclidean distance as similarity metric, we propose to use graph-domain distance to measure the patch similarity. Then we perform low-rank regularization on the similar-patch group, and incorporate a non-convex $l_{p}$ penalty to surrogate matrix rank. Finally, an alternatively minimizing strategy is employed to solve the non-convex problem. Experimental results show that our proposed method is capable of achieving more accurate reconstruction than the state-of-the-art methods in both objective and perceptual qualities.

  • adaptive multi dimension sparsity based coefficient estimation for Compression Artifact reduction
    International Conference on Multimedia and Expo, 2016
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Wen Gao
    Abstract:

    Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in Compression Artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce Compression Artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.

  • ICME - Adaptive multi-dimension sparsity based coefficient estimation for Compression Artifact reduction
    2016 IEEE International Conference on Multimedia and Expo (ICME), 2016
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Wen Gao
    Abstract:

    Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in Compression Artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce Compression Artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.

  • Compression Artifact reduction for low bit rate images based on non local similarity and across resolution coherence
    International Symposium on Circuits and Systems, 2015
    Co-Authors: Ruiqin Xiong, Xiaopeng Fan
    Abstract:

    This paper proposes a method to estimate coefficients for blocking Artifact reduction at low bit rate. Across-resolution coherence that low and high resolution image are similar is introduced to preserve signal continuity. Non-local similarity is used to provide samples for estimation by searching similar blocks of reference block. We have two sources of estimation. One source is exploiting non-local similarity to estimate coefficients of low resolution of decoded image, and interpolating the low resolution image to high resolution. We obtain the coefficients estimation for high resolution image based on the coherence across different resolutions. The other source of estimation is the quantization coefficients. These estimations are fused by their reliability respectively. Experimental results demonstrate that the proposed algorithm outperforms some recently presented methods in terms of both objective and subjective qualities of the reconstruction images.

  • ISCAS - Compression Artifact reduction for low bit-rate images based on non-local similarity and across-resolution coherence
    2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015
    Co-Authors: Jing Mu, Ruiqin Xiong, Siwei Ma
    Abstract:

    This paper proposes a method to estimate coefficients for blocking Artifact reduction at low bit rate. Across-resolution coherence that low and high resolution image are similar is introduced to preserve signal continuity. Non-local similarity is used to provide samples for estimation by searching similar blocks of reference block. We have two sources of estimation. One source is exploiting non-local similarity to estimate coefficients of low resolution of decoded image, and interpolating the low resolution image to high resolution. We obtain the coefficients estimation for high resolution image based on the coherence across different resolutions. The other source of estimation is the quantization coefficients. These estimations are fused by their reliability respectively. Experimental results demonstrate that the proposed algorithm outperforms some recently presented methods in terms of both objective and subjective qualities of the reconstruction images.

Wen Gao - One of the best experts on this subject based on the ideXlab platform.

  • graph based non convex low rank regularization for image Compression Artifact reduction
    IEEE Transactions on Image Processing, 2020
    Co-Authors: Ruiqin Xiong, Dong Liu, Xiaopeng Fan, Wen Gao
    Abstract:

    Block transform coded images usually suffer from annoying Artifacts at low bit-rates, because of the independent quantization of DCT coefficients. Image prior models play an important role in compressed image reconstruction. Natural image patches in a small neighborhood of the high-dimensional image space usually exhibit an underlying sub-manifold structure. To model the distribution of signal, we extract sub-manifold structure as prior knowledge. We utilize graph Laplacian regularization to characterize the sub-manifold structure at patch level. And similar patches are exploited as samples to estimate distribution of a particular patch. Instead of using Euclidean distance as similarity metric, we propose to use graph-domain distance to measure the patch similarity. Then we perform low-rank regularization on the similar-patch group, and incorporate a non-convex $l_{p}$ penalty to surrogate matrix rank. Finally, an alternatively minimizing strategy is employed to solve the non-convex problem. Experimental results show that our proposed method is capable of achieving more accurate reconstruction than the state-of-the-art methods in both objective and perceptual qualities.

  • adaptive multi dimension sparsity based coefficient estimation for Compression Artifact reduction
    International Conference on Multimedia and Expo, 2016
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Wen Gao
    Abstract:

    Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in Compression Artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce Compression Artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.

  • ICME - Adaptive multi-dimension sparsity based coefficient estimation for Compression Artifact reduction
    2016 IEEE International Conference on Multimedia and Expo (ICME), 2016
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Wen Gao
    Abstract:

    Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in Compression Artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce Compression Artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.

  • Video Compression Artifact Reduction via Spatio-Temporal Multi-Hypothesis Prediction
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Jiaying Liu, Weisi Lin, Wen Gao
    Abstract:

    Annoying Compression Artifacts exist in most of lossy coded videos at low bit rates, which are caused by coarse quantization of transform coefficients or motion compensation from distorted frames. In this paper, we propose a Compression Artifact reduction approach that utilizes both the spatial and the temporal correlation to form multi-hypothesis predictions from spatio-temporal similar blocks. For each transform block, three predictions with their reliabilities are estimated, respectively. The first prediction is constructed by inversely quantizing transform coefficients directly, and its reliability is determined by the variance of quantization noise. The second prediction is derived by representing each transform block with a temporal auto-regressive (TAR) model along its motion trajectory, and its corresponding reliability is estimated from local prediction errors of the TAR model. The last prediction infers the original coefficients from similar blocks in non-local regions, and its reliability is estimated based on the distribution of coefficients in these similar blocks. Finally, all the predictions are adaptively fused according to their reliabilities to restore high-quality videos. The experimental results show that the proposed method can efficiently reduce most of the Compression Artifacts and improve both subjective and objective quality of block transform coded videos.

Mei-yin Shen - One of the best experts on this subject based on the ideXlab platform.

  • ICIP (2) - Real-time Compression Artifact reduction via robust nonlinear filtering
    Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999
    Co-Authors: Mei-yin Shen
    Abstract:

    Low complexity postprocessing algorithms to reduce Compression Artifacts by using a robust nonlinear filtering approach and a table lookup method are investigated in this research. We first formulate the Compression Artifact reduction problem as a robust estimation problem. Under this framework, an enhanced image can be obtained by minimizing a cost function that accounts for the image smoothness as well as the image fidelity constraints. However, unlike traditional methods that adopt a gradient descent method to search for the optimal solution, we determine the approximate solution via the evaluation of a set of nonlinear cost functions. This nonlinear filtering process is performed to reduce the computational complexity of the postprocessing operation so that it can be implemented in real time. In the case of video postprocessing, a table lookup method is adopted to further reduce the complexity. The proposed approach is generic and flexible. It can be applied to different Compression schemes with minor finetuning. We have tested the developed algorithm on several compressed video or images which are obtained by JPEG 2000 VM, and H.263+. It has been demonstrated that the proposed method can reduce Compression Artifacts efficiently with a low computational complexity.

  • fast Compression Artifact reduction technique based on nonlinear filtering
    International Symposium on Circuits and Systems, 1999
    Co-Authors: Mei-yin Shen, Jongwon Kim, C Jay C Kuo
    Abstract:

    A computationally efficient postprocessing technique to reduce Compression Artifacts in low-bit-rate video coding is presented in this research. Blocking and ringing effects are two major Compression Artifacts observed in the block DCT-based video codec, such as H.263, when the coding bit rate becomes low. Reduction of these Artifacts can significantly improve the overall visual quality of decoded video. A new table-lookup method and a nonlinear filtering approach are adopted to remove the blocking and ringing effects, respectively. Experiments employing H.263+TMN8 anchor bit-streams are performed to show that the proposed postprocessing technique can alleviate the coding Artifacts efficiently with a low computational complexity.

  • Real-time Compression Artifact reduction via robust nonlinear filtering
    Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999
    Co-Authors: Mei-yin Shen
    Abstract:

    Low complexity postprocessing algorithms to reduce Compression Artifacts by using a robust nonlinear filtering approach and a table lookup method are investigated in this research. We first formulate the Compression Artifact reduction problem as a robust estimation problem. Under this framework, an enhanced image can be obtained by minimizing a cost function that accounts for the image smoothness as well as the image fidelity constraints. However, unlike traditional methods that adopt a gradient descent method to search for the optimal solution, we determine the approximate solution via the evaluation of a set of nonlinear cost functions. This nonlinear filtering process is performed to reduce the computational complexity of the postprocessing operation so that it can be implemented in real time. In the case of video postprocessing, a table lookup method is adopted to further reduce the complexity. The proposed approach is generic and flexible. It can be applied to different Compression schemes with minor finetuning. We have tested the developed algorithm on several compressed video or images which are obtained by JPEG 2000 VM, and H.263+. It has been demonstrated that the proposed method can reduce Compression Artifacts efficiently with a low computational complexity.

  • Artifact reduction in low bit rate wavelet coding with robust nonlinear filtering
    Multimedia Signal Processing, 1998
    Co-Authors: Mei-yin Shen
    Abstract:

    A postprocessing algorithm for Compression Artifact reduction in low-bit-rate wavelet coding is proposed in this work. We first formulate the Artifact reduction problem as a robust estimation problem. Under this framework, the Artifact-free image is obtained by minimizing a cost function that accounts for the smoothness constraint as well as image fidelity. Unlike the traditional approach that adopts gradient descent search for optimization, a set of nonlinear filters is used to calculate the approximate global minimum. The nonlinear filtering approach reduces the number of the objective function evaluation and increases the speed of convergence. It is shown by experimental results that the proposed approach can alleviate wavelet coding Artifacts efficiently with a low computational cost.

  • real time postprocessing technique for Compression Artifact reduction in low bit rate video coding
    SPIE's International Symposium on Optical Science Engineering and Instrumentation, 1998
    Co-Authors: Mei-yin Shen
    Abstract:

    A computationally efficient postprocessing technique to reduce Compression Artifacts in low-bit-rate video coding is proposed in this research. We first formulate the Artifact reduction problem as a robust estimation problem. Under this framework, the Artifact-free image is obtained by minimizing a cost function that accounts for smoothness constraints as well as image fidelity. Instead of using the traditional approach that applies the gradient descent search for optimization, a set of nonlinear filters is proposed to determine the approximating global minimum to reduce the computational complexity so that real-time postprocessing is possible. We have performed experimental results on the H.263 codec and observed that the proposed method is effective in reducing severe blocking and ringing Artifacts, while maintaining a low complexity and a low memory bandwidth.

Mainak Biswas - One of the best experts on this subject based on the ideXlab platform.

  • support vector machine svm based Compression Artifact reduction technique
    Journal of The Society for Information Display, 2007
    Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil Balram
    Abstract:

    — A Compression Artifact-reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-reduction methods specific to each type of Compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant reduction in all types of Compression Artifacts.

  • Support Vector Machine (SVM) based Compression Artifact‐reduction technique
    Journal of the Society for Information Display, 2007
    Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil Balram
    Abstract:

    — A Compression Artifact-reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-reduction methods specific to each type of Compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant reduction in all types of Compression Artifacts.

  • Compression Artifact reduction using support vector regression
    International Conference on Image Processing, 2006
    Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak Biswas
    Abstract:

    In this paper, we propose a Compression Artifact reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact reduction methods specific to each type of Compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant reduction in all types of Compression Artifacts.

  • ICIP - Compression Artifact Reduction using Support Vector Regression
    2006 International Conference on Image Processing, 2006
    Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak Biswas
    Abstract:

    In this paper, we propose a Compression Artifact reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact reduction methods specific to each type of Compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant reduction in all types of Compression Artifacts.

Xinfeng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • adaptive multi dimension sparsity based coefficient estimation for Compression Artifact reduction
    International Conference on Multimedia and Expo, 2016
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Wen Gao
    Abstract:

    Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in Compression Artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce Compression Artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.

  • Nonlocal In-Loop Filter: The Way Toward Next-Generation Video Coding?
    IEEE MultiMedia, 2016
    Co-Authors: Siwei Ma, Xinfeng Zhang, Jian Zhang, Shiqi Wang
    Abstract:

    In-loop filtering has emerged as an essential coding tool since H.264/AVC, due to its delicate design, which reduces different kinds of Compression Artifacts. However, existing in-loop filters rely only on local image correlations, largely ignoring nonlocal similarities. In this article, the authors explore the design philosophy of in-loop filters and discuss their vision for the future of in-loop filter research by examining the potential of nonlocal similarities. Specifically, the group-based sparse representation, which jointly exploits an image's local and nonlocal self-similarities, lays a novel and meaningful groundwork for in-loop filter design. Hard- and soft-thresholding filtering operations are applied to derive the sparse parameters that are appropriate for Compression Artifact reduction. Experimental results show that this in-loop filter design can significantly improve the Compression performance of the High Efficiency Video Coding (HEVC) standard, leading us in a new direction for improving Compression efficiency.

  • ICME - Adaptive multi-dimension sparsity based coefficient estimation for Compression Artifact reduction
    2016 IEEE International Conference on Multimedia and Expo (ICME), 2016
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Wen Gao
    Abstract:

    Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in Compression Artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce Compression Artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.

  • Video Compression Artifact Reduction via Spatio-Temporal Multi-Hypothesis Prediction
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong, Jiaying Liu, Weisi Lin, Wen Gao
    Abstract:

    Annoying Compression Artifacts exist in most of lossy coded videos at low bit rates, which are caused by coarse quantization of transform coefficients or motion compensation from distorted frames. In this paper, we propose a Compression Artifact reduction approach that utilizes both the spatial and the temporal correlation to form multi-hypothesis predictions from spatio-temporal similar blocks. For each transform block, three predictions with their reliabilities are estimated, respectively. The first prediction is constructed by inversely quantizing transform coefficients directly, and its reliability is determined by the variance of quantization noise. The second prediction is derived by representing each transform block with a temporal auto-regressive (TAR) model along its motion trajectory, and its corresponding reliability is estimated from local prediction errors of the TAR model. The last prediction infers the original coefficients from similar blocks in non-local regions, and its reliability is estimated based on the distribution of coefficients in these similar blocks. Finally, all the predictions are adaptively fused according to their reliabilities to restore high-quality videos. The experimental results show that the proposed method can efficiently reduce most of the Compression Artifacts and improve both subjective and objective quality of block transform coded videos.

  • Compression Artifact reduction by overlapped block transform coefficient estimation with block similarity
    IEEE Transactions on Image Processing, 2013
    Co-Authors: Xinfeng Zhang, Ruiqin Xiong
    Abstract:

    Block transform coded images usually suffer from annoying Artifacts at low bit rates, caused by the coarse quantization of transform coefficients. In this paper, we propose a new method to reduce Compression Artifacts by the overlapped-block transform coefficient estimation from non-local blocks. In the proposed method, the discrete cosine transform coefficients of each block are estimated by adaptively fusing two prediction values based on their reliabilities. One prediction is the quantized values of coefficients decoded from the compressed bitstream, whose reliability is determined by quantization steps. The other prediction is the weighted average of the coefficients in nonlocal blocks, whose reliability depends on the variance of the coefficients in these blocks. The weights are used to distinguish the effectiveness of the coefficients in nonlocal blocks to predict original coefficients and are determined by block similarity in transform domain. To solve the optimization problem, the overlapped blocks are divided into several subsets. Each subset contains nonoverlapped blocks covering the whole image and is optimized independently. Therefore, the overall optimization is reduced to a set of sub-optimization problems, which can be easily solved. Finally, we provide a strategy for parameter selection based on the Compression levels. Experimental results show that the proposed method can remarkably reduce Compression Artifacts and significantly improve both the subjective and objective qualities of block transform coded images.