An Integer Approximation Method for Discrete Sinusoidal Transforms
- URL: http://arxiv.org/abs/2007.02232v1
- Date: Sun, 5 Jul 2020 03:37:35 GMT
- Title: An Integer Approximation Method for Discrete Sinusoidal Transforms
- Authors: R. J. Cintra
- Abstract summary: We propose and analyze a class of integer transforms for the discrete Fourier, Hartley, and cosine transforms (DFT, DHT, and DCT)
The introduced method is general, applicable to several block-lengths, whereas existing approaches are usually dedicated to specific transform sizes.
New 8-point square wave approximate transforms for the DFT, DHT, and DCT are also introduced as particular cases of the introduced methodology.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Approximate methods have been considered as a means to the evaluation of
discrete transforms. In this work, we propose and analyze a class of integer
transforms for the discrete Fourier, Hartley, and cosine transforms (DFT, DHT,
and DCT), based on simple dyadic rational approximation methods. The introduced
method is general, applicable to several block-lengths, whereas existing
approaches are usually dedicated to specific transform sizes. The suggested
approximate transforms enjoy low multiplicative complexity and the
orthogonality property is achievable via matrix polar decomposition. We show
that the obtained transforms are competitive with archived methods in
literature. New 8-point square wave approximate transforms for the DFT, DHT,
and DCT are also introduced as particular cases of the introduced methodology.
Related papers
- Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling [3.866047645663101]
In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectively modeled as inverse problems.
DDPMs are shown to provide a promising solution to noisy linear inverse problems without the need for additional task specific training.
arXiv Detail & Related papers (2024-12-28T06:17:44Z) - Variable-size Symmetry-based Graph Fourier Transforms for image compression [65.7352685872625]
We propose a new family of Symmetry-based Graph Fourier Transforms of variable sizes into a coding framework.
Our proposed algorithm generates symmetric graphs on the grid by adding specific symmetrical connections between nodes.
Experiments show that SBGFTs outperform the primary transforms integrated in the explicit Multiple Transform Selection.
arXiv Detail & Related papers (2024-11-24T13:00:44Z) - Extensions on low-complexity DCT approximations for larger blocklengths based on minimal angle similarity [0.0]
The discrete cosine transform (DCT) is a central tool for image and video coding because it can be related to the Karhunen-Loeve transform (KLT)
We introduce 16-, 32-, and 64-point low-complexity DCT approximations by minimizing individually the angle between the rows of the exact DCT matrix and the matrix induced by the approximate transforms.
Fast algorithms were also developed for the low-complexity transforms, asserting a good balance between the performance and its computational cost.
arXiv Detail & Related papers (2024-10-20T01:20:35Z) - Variable Substitution and Bilinear Programming for Aligning Partially Overlapping Point Sets [48.1015832267945]
This research presents a method to meet requirements through the minimization objective function of the RPM algorithm.
A branch-and-bound (BnB) algorithm is devised, which solely branches over the parameters, thereby boosting convergence rate.
Empirical evaluations demonstrate better robustness of the proposed methodology against non-rigid deformation, positional noise, and outliers, when compared with prevailing state-of-the-art transformations.
arXiv Detail & Related papers (2024-05-14T13:28:57Z) - VI-DGP: A variational inference method with deep generative prior for
solving high-dimensional inverse problems [0.7734726150561089]
We propose a novel approximation method for estimating the high-dimensional posterior distribution.
This approach leverages a deep generative model to learn a prior model capable of generating spatially-varying parameters.
The proposed method can be fully implemented in an automatic differentiation manner.
arXiv Detail & Related papers (2023-02-22T06:48:10Z) - DCT Approximations Based on Chen's Factorization [0.17205106391379021]
Two 8-point multiplication-free DCT approximations are proposed and their fast algorithms are also derived.
Experiments with a JPEG-like image compression scheme are performed and results are compared with competing methods.
New sets of transformations are embedded into an HEVC reference software to provide a fully HEVC-compliant video coding scheme.
arXiv Detail & Related papers (2022-07-24T02:31:28Z) - Compressive Fourier collocation methods for high-dimensional diffusion
equations with periodic boundary conditions [7.80387197350208]
High-dimensional Partial Differential Equations (PDEs) are a popular mathematical modelling tool, with applications ranging from finance to computational chemistry.
Standard numerical techniques for solving these PDEs are typically affected by the curse of dimensionality.
Inspired by recent progress in sparse function approximation in high dimensions, we propose a new method called compressive Fourier collocation.
arXiv Detail & Related papers (2022-06-02T19:11:27Z) - TransCMD: Cross-Modal Decoder Equipped with Transformer for RGB-D
Salient Object Detection [86.94578023985677]
In this work, we rethink this task from the perspective of global information alignment and transformation.
Specifically, the proposed method (TransCMD) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path.
Experimental results on seven RGB-D SOD benchmark datasets demonstrate that a simple two-stream encoder-decoder framework can surpass the state-of-the-art purely CNN-based methods.
arXiv Detail & Related papers (2021-12-04T15:45:34Z) - Improving Metric Dimensionality Reduction with Distributed Topology [68.8204255655161]
DIPOLE is a dimensionality-reduction post-processing step that corrects an initial embedding by minimizing a loss functional with both a local, metric term and a global, topological term.
We observe that DIPOLE outperforms popular methods like UMAP, t-SNE, and Isomap on a number of popular datasets.
arXiv Detail & Related papers (2021-06-14T17:19:44Z) - Scalable Variational Gaussian Processes via Harmonic Kernel
Decomposition [54.07797071198249]
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.
We demonstrate that, on a range of regression and classification problems, our approach can exploit input space symmetries such as translations and reflections.
Notably, our approach achieves state-of-the-art results on CIFAR-10 among pure GP models.
arXiv Detail & Related papers (2021-06-10T18:17:57Z) - ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks [86.37110868126548]
In this work, we make use of deep residual neural networks to solve the non-stationary ODE (flow equation) based on a Euler's discretization scheme.
We illustrate these ideas on diverse registration problems of 3D shapes under complex topology-preserving transformations.
arXiv Detail & Related papers (2021-02-16T04:07:13Z) - Invertible Generative Modeling using Linear Rational Splines [11.510009152620666]
Normalizing flows attempt to model an arbitrary probability distribution through a set of invertible mappings.
The first flow designs used coupling layer mappings built upon affine transformations.
Intrepid piecewise functions as a replacement for affine transformations have attracted attention.
arXiv Detail & Related papers (2020-01-15T08:05:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.