NML computation algorithms for tree-structured multinomial Bayesian networks
EURASIP Journal on Bioinformatics and Systems Biology
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Minimax description length for signal denoising and optimized representation
IEEE Transactions on Information Theory
Wavelet thresholding via MDL for natural images
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Spatially adaptive wavelet denoising using the minimum description length principle
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This paper presents a new method for wavelet denoising using minimum description length (MDL) principle with normalized maximum likelihood density. Denoising is done by hard thresholding and a new spatially adaptive threshold which varies according to the estimated signal variance of each wavelet coefficient is derived using the MDL principle with normalized maximum likelihood density. As the normalized maximum likelihood code encodes the data with the shortest description length, smaller proportion of significant coefficients could be achieved after thresholding compared with simple MDL denoising. Thus better compression is obtained without detoriating the denoising performance measure (PSNR) compared to the MDL thresholding.