The Experts below are selected from a list of 1476 Experts worldwide ranked by ideXlab platform
Qiao Li - One of the best experts on this subject based on the ideXlab platform.
-
Using Watson perceptual model to improve Quantization index modulation based watermarking schemes
2007Co-Authors: Qiao LiAbstract:Quantization index modulation (QIM) is a popular watermarking scheme that has received considerable attention. Nevertheless, there are practical limitations of QIM. For example, traditional QIM uses a fixed Quantization Step Size, which may lead to poor fidelity in some areas of the content. More serious problems of the original QIM algorithm include its extremely sensitivity to valumetric scaling (e.g., changes in amplitude) and re-Quantization (e.g., JPEG compression). In this thesis, we first propose using Watson's perceptual model to adaptively select the Quantization Step Size based on the calculated perceptual "slack". Experimental results on 1000 images indicate improvements in fidelity as well as improved robustness in high-noise regimes. Watson's perceptual model is then modified such that the slacks scale linearly with valumetric scaling, thereby providing a QIM algorithm that is theoretically invariant to valumetric scaling. In practice, the robustness against valumetric scaling is significantly improved, but scaling can still result in errors due to cropping and roundoff that are an indirect effect of scaling. Two new algorithms are proposed the first based on regular QIM and the second based on rational dither modulation. A comparison with other methods demonstrates improved performance over other recently proposed valumetric-invariant QIM algorithms, with only small degradations in fidelity. Spread transform dither modulation (STDM) is a form of QIM that is more robust to re-Quantization. However, the robustness of STDM to JPEG compression is still poor and it remains very sensitive to valumetric scaling. We describe how a perceptual model can be incorporated into the STDM framework to (i) provide robustness to valumetric scaling, (ii) reduce the embedding-induced perceptual distortion and (iii) significantly improve the robustness to re-Quantization.
-
using perceptual models to improve fidelity and provide resistance to valumetric scaling for Quantization index modulation watermarking
IEEE Transactions on Information Forensics and Security, 2007Co-Authors: Qiao LiAbstract:Traditional Quantization index modulation (QIM) methods are based on a fixed Quantization Step Size, which may lead to poor fidelity in some areas of the content. A more serious limitation of the original QIM algorithm is its sensitivity to valumetric changes (e.g., changes in amplitude). In this paper, we first propose using Watson's perceptual model to adaptively select the Quantization Step Size based on the calculated perceptual "slack". Experimental results on 1000 images indicate improvements in fidelity as well as improved robustness in high-noise regimes. Watson's perceptual model is then modified such that the slacks scale linearly with valumetric scaling, thereby providing a QIM algorithm that is theoretically invariant to valumetric scaling. In practice, scaling can still result in errors due to cropping and roundoff that are an indirect effect of scaling. Two new algorithms are proposed - the first based on traditional QIM and the second based on rational dither modulation. A comparison with other methods demonstrates improved performance over other recently proposed valumetric-invariant QIM algorithms, with only small degradations in fidelity
-
ICASSP (2) - Using perceptual models to improve fidelity and provide invariance to valumetric scaling for Quantization index modulation watermarking
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Qiao LiAbstract:Quantization index modulation (QIM) is a computationally efficient method of watermarking with side information. This paper proposes two improvements to the original algorithm. First, the fixed Quantization Step Size is replaced with an adaptive Step Size that is determined using Watson's perceptual model. Experimental results on a database of 1000 images illustrate significant improvements in both fidelity and robustness to additive white Gaussian noise. Second, modifying the Watson model such that it scales linearly with valumetric (amplitude) scaling, results in a QIM algorithm that is invariant to valumetric scaling. Experimental results compare this algorithm with both the original QIM and an adaptive QIM and demonstrate superior performance.
David Malah - One of the best experts on this subject based on the ideXlab platform.
-
Transrating of Coded Video Signals via Optimized Index-modifled ReQuantization ?
2020Co-Authors: Michael Lavrentiev, David MalahAbstract:ReQuantization is one of the tools for bit-rate reduction of pre-encoded video to adapt it to various network bandwidth constraints. Several recent works propose using Lagrangian optimization to flnd the optimal Quantization Step, for each coded macro-block, to meet a desired rate at minimum distortion. In this paper we propose to extend the Lagrangian optimization procedure by allowing modiflcation of quantized coe‐cients values, including setting their values to zero, in addition to Quantization Step-Size selection. Thus, for each selected Step-Size the run-level values, which serve as indices in the VLC table, may get modifled so that the overall distortion for a given overall rate is reduced. Coe‐cient value modiflcation and Quantization Step-Size selection are optimally done using a low complexity trellis-based algorithm. The proposed reQuantization algorithm is implemented in an MPEG-2 environment. It provides higher PSNR values than the Lagrangianbased optimization method that only handles the selection of Quantization Steps, and still does not exceed considerably its complexity.
-
EUSIPCO - Transrating of MPEG-2 coded video via reQuantization with optimal trellis-based DCT coefficients modification
2004Co-Authors: Michael Lavrentiev, David MalahAbstract:ReQuantization is one of the tools for bit-rate reduction of pre-encoded video to adapt it to various network bandwidth constraints. Several recent works propose using Lagrangian optimization to find the optimal Quantization Step for each coded macro-block, to meet a desired rate at minimum distortion. In this paper we propose to extend the Lagrangian optimization procedure by allowing the modification of quantized coefficients values, including setting their values to zero, in addition to Quantization Step-Size selection. Coefficient value modification and Quantization Step-Size selection are optimally done using a low complexity trellis-based procedure. The proposed reQuantization algorithm provides higher PSNR values than the Lagrangian-based optimization method that only handles the selection of Quantization Steps, and still does not exceed considerably its complexity.
-
Transrating of MPEG-2 coded video via reQuantization with optimal trellis-based DCT coefficients modification
2004 12th European Signal Processing Conference, 2004Co-Authors: Michael Lavrentiev, David MalahAbstract:ReQuantization is one of the tools for bit-rate reduction of pre-encoded video to adapt it to various network bandwidth constraints. Several recent works propose using Lagrangian optimization to find the optimal Quantization Step for each coded macro-block, to meet a desired rate at minimum distortion. In this paper we propose to extend the Lagrangian optimization procedure by allowing the modification of quantized coefficients values, including setting their values to zero, in addition to Quantization Step-Size selection. Coefficient value modification and Quantization Step-Size selection are optimally done using a low complexity trellis-based procedure. The proposed reQuantization algorithm provides higher PSNR values than the Lagrangian-based optimization method that only handles the selection of Quantization Steps, and still does not exceed considerably its complexity.
Min-cheol Hong - One of the best experts on this subject based on the ideXlab platform.
-
a modified gaussian model based low complexity pre processing algorithm for h 264 video coding standard
The Journal of Korean Institute of Communications and Information Sciences, 2005Co-Authors: Wonseon Song, Min-cheol HongAbstract:In this paper, we present a low complexity modified Gaussian model based pre-processing filter to improve the performance of H.264 compressed video. Video sequence captured by general imaging system represents the degraded version due to the additive noise which decreases coding efficiency and results in unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and Quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given Quantization Step Size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and Quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.
-
a modified gaussian model based low complexity pre processing algorithm for h 264 video coding standard
Lecture Notes in Computer Science, 2004Co-Authors: Wonseon Song, Min-cheol HongAbstract:In this paper, we present a low complexity modified Gaussian model-based pre-processing filter to improve the performance of H.264 compressed video. Noisy video sequences captured by imaging system result in decline of coding efficiency and unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and Quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved, leading to improvement of visual quality and to bit-rate saving for given Quantization Step Size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and Quantization parameter induced by analyzing H.264 transformation and Quantization processes are introduced. The simulation results show the capability of the proposed algorithm.
-
A reduced complexity loop filter using coded block pattern and Quantization Step Size for H.26L video coder
ICCE. International Conference on Consumer Electronics (IEEE Cat. No.01CH37182), 2001Co-Authors: Min-cheol Hong, Huen Soo HahnAbstract:This paper addresses a reduced complexity one-dimensional loop filter to simultaneously reduce blocking and ringing artifacts of H.26L video coder. A new one-dimensional regularized smoothing functional is defined and the regularization parameters controlling the degree of smoothness to two neighboring directions are determined by coded block pattern and Quantization Step Size which are available both in the encoder and decoder. Therefore, no information is necessary to obtain the recovered image from the compressed video. The experimental results show the capability of the proposed algorithm.
-
An efficient real time algorithm to simultaneously reduce blocking and ringing artifacts of compressed video
Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999Co-Authors: Min-cheol Hong, Young Man ParkAbstract:In this paper, an efficient algorithm for simultaneous reduction of blocking and ringing artifacts of compressed video is presented. A pixel based new regularization functional, which consists of four directional regularization functions is to represent the correlation to each direction, is proposed. Also, the regularization parameter, which is critical in regularization restoration problem, is determined from the available overhead information in decoder, such as, COD (Coded Macroblock Indication) and Quantization Step Size, resulting in dramatic computational reduction to determine the regularization parameters. The experimental results show the capability and efficiency of the proposed algorithm.
Pamela Cosman - One of the best experts on this subject based on the ideXlab platform.
-
VCIP - Adaptive rate control for Wyner-Ziv video coding
2012 Visual Communications and Image Processing, 2012Co-Authors: Ghazaleh Esmaili, Pamela CosmanAbstract:In Wyner-Ziv video coding architectures, the available bit budget to each GOP is shared between key frames and Wyner-Ziv frames. In this work, we first propose a model to express the relationship between Quantization Step Size of key and WZ frames based on their motion activity. Then we apply this model to propose an adaptive algorithm adjusting the Quantization Step Size of key and WZ frames to achieve and maintain a target bit rate. We evaluate the rate distortion performance of the proposed method and compare to a common method in the literature.
-
Adaptive rate control for Wyner-Ziv video coding
2012 Visual Communications and Image Processing, 2012Co-Authors: Ghazaleh Esmaili, Pamela CosmanAbstract:In Wyner-Ziv video coding architectures, the available bit budget to each GOP is shared between key frames and Wyner-Ziv frames. In this work, we first propose a model to express the relationship between Quantization Step Size of key and WZ frames based on their motion activity. Then we apply this model to propose an adaptive algorithm adjusting the Quantization Step Size of key and WZ frames to achieve and maintain a target bit rate. We evaluate the rate distortion performance of the proposed method and compare to a common method in the literature.
Michael Lavrentiev - One of the best experts on this subject based on the ideXlab platform.
-
Transrating of Coded Video Signals via Optimized Index-modifled ReQuantization ?
2020Co-Authors: Michael Lavrentiev, David MalahAbstract:ReQuantization is one of the tools for bit-rate reduction of pre-encoded video to adapt it to various network bandwidth constraints. Several recent works propose using Lagrangian optimization to flnd the optimal Quantization Step, for each coded macro-block, to meet a desired rate at minimum distortion. In this paper we propose to extend the Lagrangian optimization procedure by allowing modiflcation of quantized coe‐cients values, including setting their values to zero, in addition to Quantization Step-Size selection. Thus, for each selected Step-Size the run-level values, which serve as indices in the VLC table, may get modifled so that the overall distortion for a given overall rate is reduced. Coe‐cient value modiflcation and Quantization Step-Size selection are optimally done using a low complexity trellis-based algorithm. The proposed reQuantization algorithm is implemented in an MPEG-2 environment. It provides higher PSNR values than the Lagrangianbased optimization method that only handles the selection of Quantization Steps, and still does not exceed considerably its complexity.
-
EUSIPCO - Transrating of MPEG-2 coded video via reQuantization with optimal trellis-based DCT coefficients modification
2004Co-Authors: Michael Lavrentiev, David MalahAbstract:ReQuantization is one of the tools for bit-rate reduction of pre-encoded video to adapt it to various network bandwidth constraints. Several recent works propose using Lagrangian optimization to find the optimal Quantization Step for each coded macro-block, to meet a desired rate at minimum distortion. In this paper we propose to extend the Lagrangian optimization procedure by allowing the modification of quantized coefficients values, including setting their values to zero, in addition to Quantization Step-Size selection. Coefficient value modification and Quantization Step-Size selection are optimally done using a low complexity trellis-based procedure. The proposed reQuantization algorithm provides higher PSNR values than the Lagrangian-based optimization method that only handles the selection of Quantization Steps, and still does not exceed considerably its complexity.
-
Transrating of MPEG-2 coded video via reQuantization with optimal trellis-based DCT coefficients modification
2004 12th European Signal Processing Conference, 2004Co-Authors: Michael Lavrentiev, David MalahAbstract:ReQuantization is one of the tools for bit-rate reduction of pre-encoded video to adapt it to various network bandwidth constraints. Several recent works propose using Lagrangian optimization to find the optimal Quantization Step for each coded macro-block, to meet a desired rate at minimum distortion. In this paper we propose to extend the Lagrangian optimization procedure by allowing the modification of quantized coefficients values, including setting their values to zero, in addition to Quantization Step-Size selection. Coefficient value modification and Quantization Step-Size selection are optimally done using a low complexity trellis-based procedure. The proposed reQuantization algorithm provides higher PSNR values than the Lagrangian-based optimization method that only handles the selection of Quantization Steps, and still does not exceed considerably its complexity.
