Improved image decompression for reduced transform coding artifacts

• Published in: IEEE transactions on circuits and systems for video technology Vol. 5; no. 6; pp. 490 - 499
• Main Authors: O'Rourke, T.P., Stevenson, R.L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.1995; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Bit rate; Constraint optimization; Degradation; Discrete cosine transforms; Exact sciences and technology; Image coding; Image processing; Image reconstruction; Information, signal and communications theory; Markov random fields; Quantization; Signal processing; Stochastic processes; Telecommunications and information theory; Transform coding; Signal compression; Stochastic model; Image processing; Noise reduction; Signal estimation; Signal processing; Algorithm; Pressure relief; Image reconstruction
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/76.475891

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 estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image that best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem that can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be 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.