2010 IEEE International Conference on Image Processing 2010
DOI: 10.1109/icip.2010.5651018
|View full text |Cite
|
Sign up to set email alerts
|

A simplified lattice structure of two-dimensional generalized lapped orthogonal transform (2-D GenLOT) for image coding

Abstract: In this paper, we propose a lattice structure of two-dimensional (2-D) linear-phase paraunitary filter banks (LPPUFBs) called as 2-D GenLOT. Muramatsu et al. have proposed a lattice structure of 2-D LPPUFBs conventionally. Our 2-D GenLOTs save design parameters and computational costs compared to the conventional 2-D LPPUFBs. We impose the regularity condition on the proposed FBs for image coding application. The 2-D GenLOTs perform an efficient image coding application.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2013
2013
2019
2019

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 8 publications
0
1
0
Order By: Relevance
“…However, multidimensional signals are generally non-separable, and this approach does not exploit their characteristics effectively. 2-D non-separable filter banks (FBs) perform more efficiently for image coding than separable FBs, because non-separable FBs may have better frequency characteristics [6], [7]. Suzuki et al proposed a lattice structure of the 2-D non-separable perfect reconstruction FBs and showed their efficiency for lossy-to-lossless image coding [8].…”
Section: Introductionmentioning
confidence: 99%
“…However, multidimensional signals are generally non-separable, and this approach does not exploit their characteristics effectively. 2-D non-separable filter banks (FBs) perform more efficiently for image coding than separable FBs, because non-separable FBs may have better frequency characteristics [6], [7]. Suzuki et al proposed a lattice structure of the 2-D non-separable perfect reconstruction FBs and showed their efficiency for lossy-to-lossless image coding [8].…”
Section: Introductionmentioning
confidence: 99%