Abstract:

This paper introduces a new model-based soft decoding technique to restore the widely used JPEG streams. The image is modeled as a 2D piecewise stationary autoregressive process, and the decoding task is formulated as an optimization problem with the constraint given by the quantization intervals which freely available at the decoder. The autoregressive model serves as an important regularization term of the objective function of the optimization. And the autoregressive model parameters are solved on the decoded image locally using a weighted total least square method, where a novel bilateral dualside weighting scheme is proposed to minimize the influence of the blocking artifact on the final 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 PSNR and subjective quality

Key words:

Image deblocking, autoregressive modeling, constrained optimization, total least squares, bilateral weighting