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Hui Yuan - One of the best experts on this subject based on the ideXlab platform.

  • model based joint Bit Allocation between geometry and color for video based 3d point cloud compression
    IEEE Transactions on Multimedia, 2020
    Co-Authors: Qi Liu, Hui Yuan, Junhui Hou, Raouf Hamzaoui
    Abstract:

    In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions. Finding an optimal Allocation of the total Bitrate between the geometry coder and the color coder is a challenging task due to the large number of possible solutions. To solve this Bit Allocation problem, we first propose analytical distortion and rate models for the geometry and color information. Using these models, we formulate the joint Bit Allocation problem as a constrained convex optimization problem and solve it with an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66% of its time complexity.

  • frame level Bit Allocation optimization based on video content characteristics for hevc
    ACM Transactions on Multimedia Computing Communications and Applications, 2020
    Co-Authors: Zhaoqing Pan, Hui Yuan, Yun Zhang, Fu Lee Wang, Sam Kwong
    Abstract:

    Rate control plays an important role in high efficiency video coding (HEVC), and Bit Allocation is the foundation of rate control. The video content characteristics are significant for Bit Allocation, and modeling an accurate relationship between video content characteristics and Bit Allocation is essential for Bit Allocation optimization. Therefore, in this article, a video content characteristics–based frame-level optimal Bit Allocation algorithm is proposed for improving the rate distortion (RD) performance of HEVC. First, the number of search points of motion estimation is used to evaluate the motion activity of video content, and the relationship between the search points and Bit Allocation is modeled as the search-points model. Second, the grey level co-occurrence matrix and temporal perceptual information are used to evaluate the spatial and temporal texture complexity, and the relationship between the video content texture complexity and Bit Allocation is modeled as the texture-complexity model. Then, the search-points model and texture-complexity model are jointly employed to allocate the coding Bits for the second and third layers of the HEVC hierarchical coding structure. Finally, the remaining coding Bits of a group-of-pictures (GOP) are allocated to the first layer of HEVC coding structure. To evaluate the performance of the proposed algorithm, the RD performance and Bitrate accuracy are used as evaluation criteria, and the experimental results show that when compared with the popularly used R-λ model–based Bit Allocation algorithm, the proposed algorithm achieves an average of -3.43% BDBR reduction and 0.13 dB BDPSNR gains with only 0.02% loss of Bitrate accuracy.

  • ssim based game theory approach for rate distortion optimized intra frame ctu level Bit Allocation
    IEEE Transactions on Multimedia, 2016
    Co-Authors: Wei Gao, Sam Kwong, Yu Zhou, Hui Yuan
    Abstract:

    A structural similarity (SSIM)-based game theory (GT) approach is proposed for rate-distortion (R-D) optimized CTU-level Bit Allocation in high efficiency video coding (HEVC). First, a SSIM-based bargaining game is formulated and the Nash bargaining solution (NBS) is proposed, in which a SSIM-based initial minimum utility is defined. Second, we propose a two-stage remaining Bit refinement-based Bit Allocation scheme. The optimization scheme of the SSIM-based bargaining game sufficiently considers the different R-D characteristics of coding tree units (CTUs), in which the feasible utility set is proved to be convex based on the proposed SSIM-based utility and R-SSIM model. Compared with the other state-of-the-art CTU-level Bit Allocation methods, the R-D performance improvements on Bjontegaard delta Bit-rate (BD-BR), Bjontegaard delta peak-signal-to-noise-ratio (BD-PSNR), and BD-SSIM metrics of the proposed method can averagely achieve significant gains, respectively. The achieved R-D performance gains have been very close to the coding performance limits from the FixedQP method. Moreover, the proposed SSIM-GT method also maintains good performances on quality smoothness, Bit rate accuracy, and encoding complexity.

  • dct coefficient distribution modeling and quality dependency analysis based frame level Bit Allocation for hevc
    IEEE Transactions on Circuits and Systems for Video Technology, 2016
    Co-Authors: Sam Kwong, Hui Yuan, Xu Wang
    Abstract:

    A frame-level Bit Allocation optimization method is proposed to improve the rate–distortion performance for High Efficiency Video Coding. First, to avoid the demerits of the mixture Laplacian distribution model on complexity, a new synthesized Laplacian distribution (SynLD) model is proposed to describe the discrete cosine transform transformed coefficients based on Kullback–Leibler-divergence analysis. Second, quality dependencies among frames are investigated, and a linear relationship between quality dependency factor (QDF) and skip-mode percentage is proposed for QDF prediction. Based on the proposed SynLD model and QDF prediction method, a $\rho $ -domain-based frame-level Bit Allocation method is proposed. Experimental results show that when compared with the state-of-the-art pixel-based unified rate–quantization (URQ) model and $R$ – $\lambda $ -model-based algorithms, 1.75- and 0.16-dB BD-peak signal-to-noise ratio (PSNR) gains can be achieved by the proposed Bit Allocation method, respectively. For quality consistency, the average PSNR standard deviation shows 0.16 and 0.02 dB lower than URQ and $R$ – $\lambda $ -model-based algorithms, respectively. The proposed method also has a much more stable buffer control status and works well for scene change cases.

  • rate distortion optimized inter view frame level Bit Allocation method for mv hevc
    IEEE Transactions on Multimedia, 2015
    Co-Authors: Hui Yuan, Sam Kwong, Xu Wang, Wei Gao, Yun Zhang
    Abstract:

    In multi-view video coding, since inter-view prediction has been adopted as an important coding tool which could improve coding efficiency greatly, inter-view dependency is inevitable, i.e., the distortion of the reference view (RV) picture could be propagated to the non-reference view (NRV) pictures . Therefore, in order to achieve higher coding efficiency , the inter-view dependency must be taken into account for inter-view Bit Allocation. In this paper, the inter-view dependency is analyzed in detail, and a rate-distortion (RD) model for NRVs is derived by taking the distortion of RV into account. Based on the derived RD model, the inter-view Bit Allocation is represented as a mathematical problem with an analytic form, and is solved by a convex optimization (Lagrangian Multiplier) method. Experimental results demonstrate that the RD performance and the inter-view quality consistency of the proposed method is better than existing methods, while the complexity of the proposed method is comparable with the existing methods.

Shahram Yousefi - One of the best experts on this subject based on the ideXlab platform.

  • single user mmwave massive mimo svd based adc Bit Allocation and combiner design
    International Conference on Signal Processing, 2018
    Co-Authors: Zakir I Ahmed, Hamid R. Sadjadpour, Shahram Yousefi
    Abstract:

    In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) Bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for Bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the Bit Allocation matrix. We attempt to minimize the CRLB with respect to the Bit Allocation matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the Bit Allocation design reduces to a set of simple inequalities consisting of ADC Bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient Bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed Bit Allocation is very close to that of the Full Search (FS) Bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based Bit Allocation of [1].

  • Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
    arXiv: Signal Processing, 2018
    Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram Yousefi
    Abstract:

    In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) Bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for Bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the Bit Allocation matrix. We attempt to minimize the CRLB with respect to the Bit Allocation matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the Bit Allocation design reduces to a set of simple inequalities consisting of ADC Bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient Bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed Bit Allocation is very close to that of the Full Search (FS) Bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based Bit Allocation of $\cite{Zakir1}$

Raouf Hamzaoui - One of the best experts on this subject based on the ideXlab platform.

  • model based joint Bit Allocation between geometry and color for video based 3d point cloud compression
    IEEE Transactions on Multimedia, 2020
    Co-Authors: Qi Liu, Hui Yuan, Junhui Hou, Raouf Hamzaoui
    Abstract:

    In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions. Finding an optimal Allocation of the total Bitrate between the geometry coder and the color coder is a challenging task due to the large number of possible solutions. To solve this Bit Allocation problem, we first propose analytical distortion and rate models for the geometry and color information. Using these models, we formulate the joint Bit Allocation problem as a constrained convex optimization problem and solve it with an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66% of its time complexity.

Qi Liu - One of the best experts on this subject based on the ideXlab platform.

  • model based joint Bit Allocation between geometry and color for video based 3d point cloud compression
    IEEE Transactions on Multimedia, 2020
    Co-Authors: Qi Liu, Hui Yuan, Junhui Hou, Raouf Hamzaoui
    Abstract:

    In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions. Finding an optimal Allocation of the total Bitrate between the geometry coder and the color coder is a challenging task due to the large number of possible solutions. To solve this Bit Allocation problem, we first propose analytical distortion and rate models for the geometry and color information. Using these models, we formulate the joint Bit Allocation problem as a constrained convex optimization problem and solve it with an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66% of its time complexity.

  • Model-based Joint Bit Allocation between Geometry and Color for Video-based 3D Point Cloud Compression
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Qi Liu, Yuan Hui, Hou Junhui, Hamzaoui Raouf, Su Honglei
    Abstract:

    Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint Bit Allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the Bit Allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future.Comment: 13pages, 10 figures, submitted to IEEE Transactions on Multimedi

Inkyu Lee - One of the best experts on this subject based on the ideXlab platform.

  • Bit Allocation and pairing methods for multi user distributed antenna systems with limited feedback
    IEEE Transactions on Communications, 2014
    Co-Authors: Hoon Lee, Haewook Park, Eunsung Park, Inkyu Lee
    Abstract:

    In this paper, we study Bit Allocation and pairing methods based on distributed zero forcing beamforming for downlink multi-user distributed antenna (DA) systems with limited feedback. Before assigning the feedback Bit for each DA port, we need to solve the pairing issue that determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the Bit Allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm that derives the pairing based on the bound of the rate loss and then obtain the non-iterative Bit Allocation method independently. To further improve the performance, an enhanced feedback Bit Allocation algorithm is also proposed by applying an iterative optimization technique. In addition, we investigate a scaling law of limited feedback systems to maintain a constant rate loss as signal-to-noise ratio increases. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional scheme for five DA port systems and verify that our analysis is well matched with the numerical results.

  • feedback Bit Allocation schemes for multi user distributed antenna systems
    IEEE Communications Letters, 2013
    Co-Authors: Eunsung Park, Heejin Kim, Haewook Park, Inkyu Lee
    Abstract:

    In this paper, we propose a feedback Bit Allocation algorithm for multi-user downlink distributed antenna systems with limited feedback. We consider a composite fading channel with small scale fadings and path loss, and assume the case where each user is served by only one distributed antenna (DA) port while each DA port can support any number of users. In order to efficiently determine Bit Allocation, we propose an iterative algorithm which minimizes an upper bound of a mean rate loss. Compared to conventional Bit Allocation methods, the proposed algorithm can be applied to more general system configurations. Simulation results show that our proposed algorithm offers a performance gain of 20% over an equal Bit Allocation scheme.

  • adaptive Bit Allocation methods for multi cell joint processing systems with limited feedback
    Personal Indoor and Mobile Radio Communications, 2011
    Co-Authors: Young-tae Kim, Seok-hwan Park, Inkyu Lee
    Abstract:

    In this paper, we study multiple-input single-output joint processing (JP) systems with limited feedback where two adjacent base stations exchange both channel state information and their data. To optimize the sum-rate performance of the JP system, we propose a new feedback Bit Allocation method which maximizes quantization accuracy in the presence of pathloss. The quantization accuracy is formulated by the expectation of the inner product between the actual channel vector and the quantized channel vector. In order to maximize the quantization accuracy, we employ a new method which compensates the phase difference of the two channels. Through numerical evaluations, we show that our proposed feedback Bit Allocation strategies provide about 50% performance gain in terms of the sum rate performance compared to the conventional method with the equal Bit Allocation scheme.