APPLICATIONS OF LEFT CIRCULANT MATRICES IN SIGNAL AND IMAGE PROCESSING

2008 ◽  
Vol 22 (04) ◽  
pp. 231-241 ◽  
Author(s):  
M. ANDRECUT
Keyword(s):  
Image Processing ◽  
Discrete Cosine Transform ◽  
Orthogonal Transformation ◽  
Circulant Matrices ◽  
Signal And Image Processing ◽  
Cosine Transform ◽  
Similar Performance ◽  
Signal Image

We show numerically that the orthogonal transformation corresponding to the eigenvectors of (random) left circulant matrices, has similar performance in signal/image processing like the discrete cosine transform.

A Digital Watermarking Algorithm of Color Image Based on Wavelet Transform

2012 ◽  
Vol 263-266 ◽  
pp. 2986-2989
Author(s):  
Yan Min Wang
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
Color Image ◽  
Color Space ◽  
Digital Watermark ◽  
Digital Audio ◽  
Cosine Transform ◽  
Digital Audio Watermarking

This paper presents an algorithm by embedding a digital watermark into an original color image. Considering the characteristics of HVS, YUV color space is employed, this algorithm presents a new adaptive digital audio watermarking based on discrete cosine transform. The result of the experiment shows that the proposed algorithm has good robustness against various image processing and attacks, good invisibility and stability.

JPEG DCT Compressiom

2013 ◽  
Vol 712-715 ◽  
pp. 2542-2545
Author(s):  
Hong Li Jia ◽  
Qiang Liu
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Digital Image ◽  
Cosine Transform ◽  
Rapid Spread ◽  
Multimedia Technologies ◽  
Joint Photographic Experts Group ◽  
Digital Image Compression ◽  
Further Development

With the rapid spread of image processing applications and the further development of multimedia technologies, compression standards become more and more important. This paper intends to explain JPEG (Joint Photographic Experts Group) compression, which is currently a worldwide standard for digital image compression, is based on the discrete cosine transform (DCT). Based on the research, the paper describes theory and algorithms of the JPEG DCT compression and implements a baseline JPEG codec (encoder/decoder) with MATLAB.

scholarly journals Implementation of Modified Signed Discrete Cosine Transform

2019 ◽  
Vol 8 (9) ◽  
pp. 807-809
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
High Power ◽  
Flow Diagram ◽  
Discrete Cosine Transforms ◽  
Cosine Transform ◽  
Speed Up ◽  
Signum Function

One of the most important properties of Discrete Cosine Transform (DCT) is high power compaction, due to this property DCT is used for coding, image processing, image compression etc. The DCT application can be speed up by Signed Discrete Cosine Transforms (SDCT). The SDCT is the modification approximates of the DCT. In this paper by proposing signum function we proposed a flow diagram of algorithm and its architecture by considering 8 point transforms.

Discrete Cosine Transform Method for Watermarking in Digital Image Processing

2021 ◽  
Author(s):  
Nur Lukman ◽  
Jumadi Jumadi ◽  
Muhammad Faris Aminuddin ◽  
Dian Sa'adillah Maylawati ◽  
Nunik Destria Arianti ◽  
...  
Keyword(s):  
Image Processing ◽  
Discrete Cosine Transform ◽  
Digital Image Processing ◽  
Digital Image ◽  
Transform Method ◽  
Cosine Transform

scholarly journals A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

2021 ◽  
Vol 7 (10) ◽  
pp. 218
Author(s):  
Mohamed Hamidi ◽  
Mohamed El Haziti ◽  
Hocine Cherifi ◽  
Mohammed El Hassouni
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Extraction Process ◽  
Alternative Methods ◽  
Discrete Wavelet ◽  
Scale Invariant ◽  
Cosine Transform

In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

Elastic pre-stack seismic inversion through Discrete Cosine Transform reparameterization and Convolutional Neural Networks

Geophysics ◽  
2020 ◽  
pp. 1-70
Author(s):  
Mattia ALEARDI ◽  
Alessandro Salusti
Keyword(s):  
Discrete Cosine Transform ◽  
Elastic Properties ◽  
Seismic Data ◽  
Orthogonal Transformation ◽  
Seismic Inversion ◽  
Joint Probability Distribution ◽  
Elastic Model ◽  
Inversion Algorithm ◽  
Elastic Parameters ◽  
Cosine Transform

We develop a pre-stack inversion algorithm that combines a Discrete Cosine Transform (DCT) reparameterization of data and model spaces with a Convolutional Neural Network (CNN). The CNN is trained to predict the mapping between the DCT-transformed seismic data and the DCT-transformed 2-D elastic model. A convolutional forward modeling based on the full Zoeppritz equations constitutes the link between the elastic properties and the seismic data. The direct sequential co-simulation algorithm with joint probability distribution is used to generate the training and validation datasets under the assumption of a stationary non-parametric prior and a Gaussian variogram model for the elastic properties. The DCT is an orthogonal transformation that is here used as an additional feature extraction technique that reduces the number of unknown parameters in the inversion and the dimensionality of the input and output of the network. The DCT reparameterization also acts as a regularization operator in the model space and allows for the preservation of the lateral and vertical continuity of the elastic properties in the recovered solution. We also implement a Monte Carlo simulation strategy that propagates onto the estimated elastic model the uncertainties related to both noise contamination and network approximation. We focus on synthetic inversions on a realistic subsurface model that mimics a real gas-saturated reservoir hosted in a turbiditic sequence. We compare the outcomes of the implemented algorithm with those provided by a popular linear inversion approach and we also assess the robustness of the CNN inversion to errors in the estimated source wavelet and to erroneous assumptions about the noise statistic. Our tests confirm the applicability of the proposed approach, opening the possibility of estimating the subsurface elastic parameters and the associated uncertainties in near real-time while satisfactorily preserving the assumed spatial variability and the statistical properties of the elastic parameters.

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