December 14, 2013

Multirate structures, multiscale decompositions: two years after

Signal Processing
Two years ago, in December 2011, a special issue of Signal Processing was published on the theme of Advances in Multirate Filter Bank Structures and Multiscale Representations. Eleven papers, ranging from 1D or 2D data, spanning topics from filter frame design to compression, browsing applications from audio to medical imaging. This issue was very rich in interesting papers, thanks to the authors and reviewers.

Can one say a little more? Of course, bibliometrics or scientometrics generate a lot of debates. Generally, such indicators do not mean anything absolute, they may only serve as a ground for discussion. Let us just compare the figures with the journal statistics: Signal Processing has a two-year Impact Factor on 1.851 (2012). Special issue citation data is tabulated in the following array:

Citation sources (2013/12/14) Elsevier ISI-Thomson Google
Title Scopus WoS Scholar
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity 13 11 20
Augmented Lagrangian based Reconstruction of non-uniformly sub-Nyquist sampled MRI data 13 7 18
Bandlet Image Estimation with Model Selection 1 0 2
Matching Pursuit Shrinkage in Hilbert Spaces 0 0 2
Non Separable Lifting Scheme with Adaptive Update Step for Still and Stereo Image Coding 3 2 7
Multivariate empirical mode decomposition and application to multichannel filtering 11 5 14
Resonance-Based Signal Decomposition: A New Sparsity-Enabled Signal Analysis Method 18 5 24
Activelets: Wavelets for Sparse Representation of Hemodynamic Responses 10 7 12
Fast orthogonal sparse approximation algorithms over local dictionaries 4 2 6
Recursive Nearest Neighbor Search in a Sparse and Multiscale Domain for Comparing Audio Signals 0 0 0
Symmetric Tight Frame Wavelets With Dilation Factor M=4 1 0 1

One observes that the citation counts for Elsevier Scopus, ISI-Thomson Web of Science or Google Scholar are very much uneven. As usual, Google Scholar lies above the two others. This observation should suffice, at least for genuine data scientists, to refrain from using carelessly a single number such as the h-index, without citing the source. When a reality (one's paper visibility) is given three very different values by three similar sensors (with different vendors), one should be cautious about using only the sensor value she-he prefers. This attitude should be very uncoherent for people claiming they can denoise measurements, restore signals, analyze images with precise tools. And forget all about the scientific method when it comes to quantified self-performance.

Then the counts are very different for the different papers. So an average index (here 3.5, or 2.8 without the overview paper) is not meaningful. One potential sound approach is to resort to range statistics, with the least favorable index (ISI-Thomson-WoS). Four papers have not been cited yet. The seven others have a citation count [11, 7, 7, 5, 5, 2, 2] greater than the impact factor (1.851). Qualitatively, the performance of this special issue may be said a little above the journal's performance.

Of course, the eleven papers have a longer life ahead than a two-year run.The only thing we may wish is an absolute improvement of their visibility and influence. Meet you in December 2015, to see how the pack has grown. Here is the paper leaflet.

Keywords: Review; Multiscale; Geometric representations; Oriented decompositions; Scale-space; Wavelets; Atoms; Sparsity; Redundancy; Bases; Frames; Edges; Textures; Image processing; Haar wavelet; Non-Euclidean wavelets; Augmented Lagrangian methods; MRI reconstruction; Non-uniform Fourier transform; Shearlet; Compressed sensing; Model selection; White noise model; Image estimation; Geometrically regular functions; Bandlets; Dictionary; Matching pursuit; Shrinkage; Sparse representation; Lossless compression; Progressive reconstruction; Lifting schemes; Separable transforms; Non-separable transforms; Adaptive transforms; Multiresolution analysis; Wavelets; Stereo coding; Mono- and multivariate empirical mode decomposition; Filter bank structure; Electroencephalography data analysis; Sparse signal representation; Constant-Q transform; Wavelet transform; Morphological component analysis; BOLD fMRI; Hemodynamic response; Wavelet design; Sparsity; l1 minimization; Sparse approximation; Greedy algorithms; Shift invariance; Orthogonal Matching Pursuit; Multiscale decomposition; Sparse approximation; Time—frequency dictionary; Audio similarity; Wavelet transform; Frame; Symmetric filterbanks; Multiresolution analysis