IEEE Transactions on Image Processing
Image deblocking by the dual adaptive FIR wiener filter and overcomplete representation
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Image postprocessing by Non-local Kuan's filter
Journal of Visual Communication and Image Representation
Reduction of JPEG compression artifacts by kernel regression and probabilistic self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Image deblocking via sparse representation
Image Communication
Non-causal temporal prior for video deblocking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
Adaptive non-local means filter for image deblocking
Image Communication
Proceedings of the first ACM workshop on Information hiding and multimedia security
Perceptually optimized blind repair of natural images
Image Communication
| Hi-index | 0.01 |
Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we propose to solve it by the maximum a posteriori criterion. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field based on the fields of experts framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality. In addition, we examine the noise model used and its parameter setting. The noise model assumes that the DCT coefficients and their quantization errors are independent. This assumption is no longer valid when the coefficients are truncated. We explain how this problem can be rectified using the current parameter setting.