Optimal Design of Oversampled Synthesis FBs With Lattice Structure Constraints

Chai, Lu; Zhang, Jingxin; Sheng, Yuxia

doi:10.1109/tsp.2011.2147783

Cited by 18 publications

(3 citation statements)

References 38 publications

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“…As can be seen from Tables 3 and 4, the filter banks yielded in this paper are not inferior to published ones in terms of CG. For the 7 Â 21 CLLPPRFB, our obtained filter bank DCT- 2 3 À 3 0 shows better CG than all published ones, including the one in [17]. Then let us compare the filter banks produced in this paper and those published in [17] for other considered cases, i.e.…”

Section: Design Examplesmentioning

confidence: 89%

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A method to initialize free parameters in lattice structure of arbitrary-length linear phase perfect reconstruction filter bank

Gao

2015

Signal Processing

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Section: Design Examplesmentioning

confidence: 89%

“…One can efficiently construct LPPRFB by lattice structure [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], which yields filter bank with free parameters. Optimization of the free parameters will produce more practical filter bank.…”

Section: Introductionmentioning

confidence: 99%

A method to initialize free parameters in lattice structure of arbitrary-length linear phase perfect reconstruction filter bank

Gao

2015

Signal Processing

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“…R > 1, there is an infinite number of perfect reconstruction (PR) combination of analysis and synthesis systems [32,33]. It can be verified that the above pair of systems constitute a PR system together.…”

Section: Parseval Tight Frame Constraintmentioning

confidence: 99%

Structured dictionary learning with 2-D non-separable oversampled lapped transform

Muramatsu

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work proposes a novel design method of a two-dimensional (2-D) Non-Separable Oversampled Lapped Transform (NSOLT) for a given image by introducing a typical two stage procedure of dictionary learning. NSOLT is a lattice-structure-based transform and yields a redundant dictionary of which atoms satisfy the nonseparable, symmetric, real-valued, overlapping and compact-support property. In addition, the Parseval tight frame constraint can structurally be imposed, while the redundancy R is flexibly controlled by the ratio of the number of channels P and the downsampling ratio M . Compared with the other dictionary learning approaches, the proposed method is moderately structured so that it is capable of multiscale construction as well as atom termination at image boundary. The significance of the proposed method is verified by showing an example of learned dictionary and sparse approximation results.

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An Improved Lattice Filter Structure with Minimum Roundoff Noise Gain

Huang

Zhang

et al. 2016

Circuits Syst Signal Process

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Optimal Design of Oversampled Synthesis FBs With Lattice Structure Constraints

Cited by 18 publications

References 38 publications

A method to initialize free parameters in lattice structure of arbitrary-length linear phase perfect reconstruction filter bank

A method to initialize free parameters in lattice structure of arbitrary-length linear phase perfect reconstruction filter bank

Structured dictionary learning with 2-D non-separable oversampled lapped transform

An Improved Lattice Filter Structure with Minimum Roundoff Noise Gain

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