scispace - formally typeset
Journal ArticleDOI

Combined techniques of singular value decomposition and vector quantization for image coding

Reads0
Chats0
TLDR
In this paper, the combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding.
Abstract
The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity. >

read more

Citations
More filters
Proceedings ArticleDOI

Robust DWT-SVD domain image watermarking: embedding data in all frequencies

TL;DR: A hybrid scheme based on DWT and Singular Value Decomposition (SVD) is presented, which allows the development of a watermarking scheme that is robust to a wide range of attacks.
Journal ArticleDOI

An Efficient SVD-Based Method for Image Denoising

TL;DR: The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
Book ChapterDOI

Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications

TL;DR: In this paper, an exact analytic technique is developed that facilitates the estimation of the Jacobian using calculations based on simple linear algebra, which is very useful in certain applications involving multivariate regression or the computation of the uncertainty related to estimates obtained through the Singular Value Decomposition.
Journal ArticleDOI

Robust embedding of visual watermarks using discrete wavelet transform and singular value decomposition

TL;DR: A hybrid nonblind scheme based on DWT and singular value decomposition (SVD) is presented, and it is shown that it is considerably more robust and reliable than a pure SVD-based scheme.
Proceedings ArticleDOI

Digital image watermarking using singular value decomposition

TL;DR: Simulation results are provided which demonstrate the robustness of the proposed technique to a variety of common image degradations and the results of the approach are compared to other transform domain watermarking methods.
References
More filters
Book

Matrix computations

Gene H. Golub
Journal ArticleDOI

An Algorithm for Vector Quantizer Design

TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Journal ArticleDOI

Discrete Cosine Transform

TL;DR: In this article, a discrete cosine transform (DCT) is defined and an algorithm to compute it using the fast Fourier transform is developed, which can be used in the area of digital processing for the purposes of pattern recognition and Wiener filtering.
Journal Article

Vector quantization

TL;DR: During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes.
Related Papers (5)