Microarray image compression: SLOCO and the effect of information loss
Signal Processing - Special issue: Genomic signal processing
Journal of Mathematical Imaging and Vision
Automatic parameter selection for a k-segments algorithm for computing principal curves
Pattern Recognition Letters
Denoising of multicomponent images using wavelet least-squares estimators
Image and Vision Computing
Improved spatially adaptive MDL denoising of images using normalized maximum likelihood density
Image and Vision Computing
IEEE Transactions on Signal Processing
Image denoising in contourlet domain based on a normal inverse Gaussian prior
Digital Signal Processing
Additive noise removal using a novel fuzzy-based filter
Computers and Electrical Engineering
A new fuzzy-based wavelet shrinkage image denoising technique
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Shift-Invariant image denoising using mixture of laplace distributions in wavelet-domain
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We study the application of Rissanen's (1989) principle of minimum description length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coefficients. We find that this approach leads to an adaptive thresholding rule. While achieving mean-squared-error performance comparable with other popular thresholding schemes, the MDL procedure tends to keep far fewer coefficients. From this property, we demonstrate that our method is an excellent tool for simultaneous denoising and compression. We make this claim precise by analyzing MDL thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of nonzero coefficients