Enhancement of JPEG-Compressed Images by Re-application of JPEG
Journal of VLSI Signal Processing Systems - Special issue on multimedia signal processing
Reduction of blocking artifacts in JPEG compressed images
Digital Signal Processing
Combined frequency and spatial domain algorithm for the removal of blocking artifacts
EURASIP Journal on Applied Signal Processing
Temporal resolution enhancement in compressed video sequences
EURASIP Journal on Applied Signal Processing
Design a deblocking filter with three separate modes in DCT-based coding
Journal of Visual Communication and Image Representation
A speech enhancement algorithm based on a chi MRF model of the speech STFT amplitudes
IEEE Transactions on Audio, Speech, and Language Processing
A document image model and estimation algorithm for optimized JPEG decompression
IEEE Transactions on Image Processing
An image coding method by construction of variable block size and region compensation
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
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
Learning-based image restoration for compressed images
Image Communication
Content-adaptive deblocking for high efficiency video coding
Image Communication
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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