Improved image decompression for reduced transform coding artifacts

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Abstract
The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimate technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non- Gaussian Markov random field image model. The estimation of the best reconstructed image results in a convex constrained optimization problem which can be solved iteratively. Experimental results are shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.

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