JPEG stream soft-decoding technique based on autoregressive modeling  

作　　者：NIU Yi SHI Guang-ming WANG Xiao-tian WANG Li-zhi GAO Da-hua 

机构地区：[1]School of Electronic Engineering, Xidian University, Xi'an 710071, China [2]ECE McMaster University, Hamilton L8S 4KI, Canada [3]School of Science, Air Force Engineering University, Xi'an 710051, China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2012年第5期115-123,共9页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61033004,61070138,61072104,61003148)

摘　　要：This paper introduces a new model-based soft decoding techniqt, e to restore the widely used joint photographic expert group （JPEG） streams. The image is modeled as a two dimensional （2D） piecewise stationary autoregressive process, and the decoding task is formulated as a constrained optimization problem. All the constraints are given by the quantization intervals which available at the decoder freely. The autoregressive model serves as an important regularization term of the objective function of the optimization, and the model parameters are solved on the decoded image locally using a weighted total least square method. In addition, a novel bilateral dualside weighting scheme is proposed to minimize the influence of the blocking artifact on the accuracy of parameter estimation. Extensive experimental results suggest that the proposed algorithm systematically improves the quality of JPEG images and also outperforms existing JPEG postprocessing algorithms in a wide bit-rate range both in terms of peak signal-to-noise ratio （PSNR） and subjective qualityThis paper introduces a new model-based soft decoding techniqt, e to restore the widely used joint photographic expert group （JPEG） streams. The image is modeled as a two dimensional （2D） piecewise stationary autoregressive process, and the decoding task is formulated as a constrained optimization problem. All the constraints are given by the quantization intervals which available at the decoder freely. The autoregressive model serves as an important regularization term of the objective function of the optimization, and the model parameters are solved on the decoded image locally using a weighted total least square method. In addition, a novel bilateral dualside weighting scheme is proposed to minimize the influence of the blocking artifact on the accuracy of parameter estimation. Extensive experimental results suggest that the proposed algorithm systematically improves the quality of JPEG images and also outperforms existing JPEG postprocessing algorithms in a wide bit-rate range both in terms of peak signal-to-noise ratio （PSNR） and subjective quality

关 键 词：image deblocking autoregressive modeling constrained optimization total least squares bilateral weighting 

分 类 号：TN975[电子电信—信号与信息处理] TP311.56[电子电信—信息与通信工程]