@article{yang1995projection,
abstract = {At the present time, block-transform coding is probably the most popular approach for image compression. For this approach, the compressed images are decoded using only the transmitted transform data. In this paper, we formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. Numerical experiments are shown that demonstrate that the proposed algorithms work better than both the JPEG deblocking recommendation and our previous projection-based image decoding approach. {\textcopyright} 1995 IEEE},
author = {{Yongyi Yang} and Galatsanos, Nikolas P. N.P. and Katsaggelos, Aggelos K. A.K. and Yang, Yongyi and Galatsanos, Nikolas P. N.P. and Katsaggelos, Aggelos K. A.K.},
doi = {10.1109/83.392332},
institution = {SPIE INTERNATIONAL SOCIETY FOR OPTICAL},
issn = {19410042},
journal = {IEEE Transactions on Image Processing},
month = {jul},
number = {7},
pages = {896--908},
publisher = {IEEE},
title = {{Projection-Based Spatially Adaptive Reconstruction of Block-Transform Compressed Images}},
url = {http://ieeexplore.ieee.org/document/392332/},
volume = {4},
year = {1995}
}

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Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. 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For this approach, the compressed images are decoded using only the transmitted transform data. In this paper, we formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. 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