Abstract:To remove the block reconstruction artifacts from image decoder, which are generated by block-based discrete cosine transform (BDCT), this paper proposes a block removing algorithm with two kinds of joint priors that are reweighted low-rank approximation and Gaussian mixture model respectively. Firstly, it adopts reweighted low-rank approximation to enforce the intrinsic local structure and nonlocal self-similarity existed among blocks from the image, which is believed to better preserve the fine structure existed in the original image. Then, the Gaussian mixture model is explicitly incorporated to model the blocking artifacts, which helps to obtain a more reliable and robust solution. Experimental results demonstrate that the proposed algorithm outperforms other comparison deblocking methods in both visual perception and objective evaluators.