The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform

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

  • Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017
    Co-Authors: Cui Jing, Wang Shanshe, Wang Shiqi, Zhang Xinfeng, Ma Siwei, Gao Wen
    Abstract:

    Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each Transformed Coefficient and the determination of best quantized Coefficients for transform units with the minimum total cost, to softly optimize the quantized Coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform Coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the Coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%-0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.National Natural Science Foundation of China [61632001, 61571017, 61421062]; National Basic Research Program of China (973 Program) [2015CB351800]; Shenzhen Peacock PlanSCI(E)ARTICLE83802-38162

Chen Ting - One of the best experts on this subject based on the ideXlab platform.

  • Transformed Coefficient reuse in adaptive block-size transform scheme for H.264/AVC encoder
    Application Research of Computers, 2010
    Co-Authors: Chen Ting
    Abstract:

    Proposed a 4×4 transform Coefficient reuse algorithm based on single instruction multiple data (SIMD) instruction set architecture for reducing the computing complexity of adaptive block-size transform(ABT) scheme in H.264/AVC.In ABT scheme,the algorithm computed the 8×8 transform and quantization Coefficients with an 8×8 derivate transform and transform through reusing the 4×4 transform Coefficients.The proposed algorithm could be simply implemented in SIMD instruction set architecture,such as PC and DSP,and reduced the computing time and resource consumption of ABT scheme significantly.The simulation results show that the transform Coefficients reusing algorithm can save computing time of 8×8 transform up to 50%.

  • Transformed Coefficient reuse in adaptive block size transform scheme for h 264 avc encoder
    Application Research of Computers, 2010
    Co-Authors: Chen Ting
    Abstract:

    Proposed a 4×4 transform Coefficient reuse algorithm based on single instruction multiple data (SIMD) instruction set architecture for reducing the computing complexity of adaptive block-size transform(ABT) scheme in H.264/AVC.In ABT scheme,the algorithm computed the 8×8 transform and quantization Coefficients with an 8×8 derivate transform and transform through reusing the 4×4 transform Coefficients.The proposed algorithm could be simply implemented in SIMD instruction set architecture,such as PC and DSP,and reduced the computing time and resource consumption of ABT scheme significantly.The simulation results show that the transform Coefficients reusing algorithm can save computing time of 8×8 transform up to 50%.

Cui Jing - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017
    Co-Authors: Cui Jing, Wang Shanshe, Wang Shiqi, Zhang Xinfeng, Ma Siwei, Gao Wen
    Abstract:

    Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each Transformed Coefficient and the determination of best quantized Coefficients for transform units with the minimum total cost, to softly optimize the quantized Coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform Coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the Coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%-0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.National Natural Science Foundation of China [61632001, 61571017, 61421062]; National Basic Research Program of China (973 Program) [2015CB351800]; Shenzhen Peacock PlanSCI(E)ARTICLE83802-38162

Ma Siwei - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017
    Co-Authors: Cui Jing, Wang Shanshe, Wang Shiqi, Zhang Xinfeng, Ma Siwei, Gao Wen
    Abstract:

    Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each Transformed Coefficient and the determination of best quantized Coefficients for transform units with the minimum total cost, to softly optimize the quantized Coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform Coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the Coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%-0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.National Natural Science Foundation of China [61632001, 61571017, 61421062]; National Basic Research Program of China (973 Program) [2015CB351800]; Shenzhen Peacock PlanSCI(E)ARTICLE83802-38162

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

  • Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017
    Co-Authors: Cui Jing, Wang Shanshe, Wang Shiqi, Zhang Xinfeng, Ma Siwei, Gao Wen
    Abstract:

    Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each Transformed Coefficient and the determination of best quantized Coefficients for transform units with the minimum total cost, to softly optimize the quantized Coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform Coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the Coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%-0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.National Natural Science Foundation of China [61632001, 61571017, 61421062]; National Basic Research Program of China (973 Program) [2015CB351800]; Shenzhen Peacock PlanSCI(E)ARTICLE83802-38162