Discrete Cosine Transform Method Research Articles

Discrete Cosine Transform Algorithm Analysis of The Application of QR Codes on Birth Certificate Image Objects

Security and authentication of important documents such as birth certificates have become a major concern today and in the digital age. In this context, the use of watermarking technology is an effective solution to add additional information to digital documents without destroying their authenticity. The DCT method is used to convert images into the frequency domain, allowing the watermark to be added without disturbing the appearance of the document. Testing ensures that the watermarking process does not damage the appearance of the original document and that the watermark information can be accurately re-extracted. The research results show that the QR code watermark using the DCT method successfully provides secure and authentic birth certificates. The application of the Discrete Cosine Transform (DCT) method to the birth certificate QR code watermarking process was able to significantly improve document security and authentication without compromising document quality or important information. This thesis outlines the extensive steps in the practical application of the QR code watermark on birth certificates. From data preparation including birth certificate images and QR codes to the watermark conversion and extraction process, all steps are explained in detail. Thus, applying this watermarking technique can be an effective solution to protect the integrity of birth certificates and similar documents in the digital environment.

In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform

Digital image watermarking is a crucial technique used to protect the integrity and ownership of digital images by embedding imperceptible watermarks into the image content. This review concentrates on the utilization of discrete cosine transform (DCT) and discrete wavelet transform (DWT) in digital image watermarking schemes. DCT, widely used in image compression like JPEG, is an attractive choice for watermarking, modifying DCT coefficients with minimal impact on image quality. On the other hand, DWT offers multiresolution representation, enabling better localization and robustness against attacks. DWT-based methods use wavelet coefficients to embed watermarks in specific frequency bands or image regions. The review examines the strengths and weaknesses of DCT and DWT in digital image watermarking, exploring algorithms and approaches proposed in the literature. It also addresses challenges like attacks, synchronization, and robustness to image processing. Additionally, a comparative analysis of DCT and DWT-based methods considers imperceptibility, robustness, capacity, and computational complexity. By offering valuable insights, this review aids researchers and practitioners in implementing secure and efficient digital image watermarking solutions.

Inter-Intra Modal Representation Augmentation With DCT-Transformer Adversarial Network for Image-Text Matching

Image-text matching has become a challenging task in the multimedia analysis field. Many advanced methods have been used to explore local and global cross-modal correspondence in matching. However, most methods ignore the importance of eliminating potential irrelevant features in the original features of each modality and cross-modal common feature. Moreover, the features extracted from regions in images and words in sentences contain cluttered background noise and different occlusion noise, which negatively affects alignment. Different from these methods, we propose a novel DCT-Transformer Adversarial Network (DTAN) for image-text matching in this paper. This work can obtain an effective metric based on two aspects: i) DCT-Transformer uses DCT (Discrete Cosine Transform) method based on a transformer mechanism to extract multi-domain common representations and eliminate irrelevant features from different modalities (inter-modal). Among them, DCT divides multi-modal content into chunks of different frequencies and quantifies them. ii) The adversarial network introduces an adversary idea by combining the original features of various single modalities and the multi-domain common representation, alleviating the background noise within each modality (intra-modal). The proposed adversarial feature augmentation method can easily obtain the common representation that is only useful for alignment. Extensive experiments are completed on the benchmark datasets Flickr30K and MS-COCO, demonstrating the superiority of the DTAN model over the state-of-the-art methods.

SoC FPGA-Based Rapid Prototyping of Compressed, Secured and Wireless Image Transmission for Wildlife Surveillance System

Wildlife plays an important role in balancing the earth’s ecosystem. Today, many species of wildlife are threatened with extinction. Images are important input data for wildlife surveillance technology,whichallowsreal situations in the field to be observed and analysed. This project proposed a wireless transmission systemof compressedandsecured imagesusing the SoCFPGA platformfor improving the existing systemin terms of image data collection. Image compression ensuredthat the proposed system wasefficient in terms of data processing, transmission and storage. Image data encryption aimed at ensuring that the security of the transmitted image data would not be hijacked by poachers or non-authorities and ultimately this system wouldtransmit compressed and secured images wirelessly to provide real-time data. The prototype usedLabVIEW 2017 software and NI myRIO (as image transmitter) and a computer (as image receiver) hardware. This platform has been utilisedfor the development of the architectural design of a compression system using the discrete cosine transform (DCT) method, the development of an encryption system using the advanced encryption standards (AES) method and wireless transmission using Wi-Fi. The implementation of DCT on the field-programmable gate array (FPGA) platform allowedthe imagesto be compressed by an average of 44% to shortenthe time used in the transmission process. The AES method guaranteeddata security with images that can only be viewedwhenusing the correct password. Combining both the DCT and AES methods, the rapid prototyping of the wireless transmission of compressed and secured images for a wildlife surveillance system equippedwith a motion sensor and a camera was developed to detect motion, capture imagesand transmit three (3) different image resolutions atdistances of up to 60m.

Application of Double Pictures to Audio Watermarking using Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) Methods

One way to control the development of arbitrary distribution of audio is to apply audio watermarking. Audio Watermarking is used to identify the ownership rights of audio files by embedding a watermark while paying attention to the audio quality and the watermark's resistance to amplify, resampling, and invert attacks. Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) methods are frequency-domain methods that have a good robustness. The DCT and SVD methods are combined with inserting and extracting watermarks using two images as watermarks. From the test results, the effect of inserting an audio watermark with two images does not significantly impact the durability of the watermark and the quality of the audio inserted with the watermark. However, the alpha value has a significant effect. The higher the alpha value, the lower the audio SNR value by an average of 1.93 dB from an alpha value of 0.0008 and 0.001 for each type of classical, jazz, and rock audio genre.

A hybrid compression method for the NMR data based on window averaging and Discrete Cosine Transform

A large amount of raw echo data is collected by NMR logging, especially in multi-dimensional NMR logging, and a direct inversion for the raw echo data is time-consuming, so the echo data collected need to be compressed before inversion to improve inversion speed. This paper proposes a multi-dimensional NMR data compression method based on Discrete Cosine Transform (DCT). Utilizing the energy concentration characteristics of DCT, the low-frequency information of the echo data is retained to realize the compression of raw echo data. On this basis, a hybrid compression (WAC-DCT) method is proposed, which uses the window averaging method (WAC) to pre-compress the raw echo data, and then uses the DCT compression method to compress the pre-compressed echo data again to achieve a high compression rate. The compression effects of the WAC method, the FastSVD method, the WAC-SVD method, the DCT method and the WAC-DCT method are compared by numerical simulation. The results show that some fluid information is lost in the DCT compression process with a low compression rate. The FastSVD method, the WAC-SVD method and the WAC-DCT method all have the characteristic of a high compression rate. When the number of compressed echoes is very small, the WAC-DCT method has obvious advantage in T1 distribution. Compared with the case of without compression, the WAC-DCT method not only ensures a high compression rate, but also effectively reduces the inversion time, which has a good application effect in multi-dimensional NMR data compression.

Comparison of Steganography Using the Discrete Cosine Transform Method on Image Based Bilinear, Nearest Neighbor and Spline Interpolation

The research was conducted in the field of steganography. Discrete Cosine Transform (DCT) is a method used in the insertion technique. The results of steganography have problems if they look blurry, have low levels of similarity and high error values. One way to solve this problem is by proposing image interpolation. The interpolation method consists of various kinds and gives each other advantages. This study intends to compare three kinds of interpolation techniques to find the best one. The three interpolation techniques are bilinear, nearest neighbor, and spline. The method used in this research is experimental. Images with extension formats * .tif, * .png, and * .bmp with dimensions of 512x512 px are interpolated by scaling 1.5, 2, and 4. The results of the interpolation process are used to insert messages in * .txt format of 157 bytes with discrete cosines transform (DCT). The image quality of the message insertion is measured by the MSE and PSNR values. The result of the message insertion test shows that the value of the image quality is directly proportional, meaning that if the condition of the message size is fixed and the cover dimensions are greater, the MSE value will be smaller and the PSNR value will be greater. Images with * .tif and * .bmp extension formats have good stability, * .png images vary in size. The smallest error value test results were obtained in the spline interpolation technique and this method when compared to the other two techniques had the lowest average MSE value of 8.221 and the PSNR value of 40,301 dB.

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI.

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

A robust Plenoptic image watermarking method using graph-based transform

Numerous methods have been introduced for digital images watermarking. But there are fewer studies on plenoptic images. This image has more information (such as depth) and costs more than regular digital images. So, protecting the ownership of plenoptic is important. In this paper a robust plenoptic image watermarking has been introduced which is based on Graph-based Transform (GBT). Also, in order to maximizing the robustness of watermarking an optimization has been used to find the best graph structure of GBT. This optimization has done using a Genetic Algorithm. One of the common ways to robust the watermarking is the use of discrete cosine transform (DCT). In this study, we have shown that the proposed method is much more powerful than DCT. The proposed method is tested on three plenoptic images namely Alley, Basic Shapes and Coffee Table and the results show that the proposed method is more robust to similar methods such as DCT methods. Watermarking was evaluated using BER, SSIM and PSNR criteria and Gaussian noise attacks with varying intensities.

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

In the past decade, a transform called all phase discrete cosine biorthogonal transform (APDCBT) appeared in the field of digital image processing. It is mainly used to solve the negativity found in discrete cosine transform (DCT), especially in a low bit rate. In this paper, the APDCBT is employed for watermark insertion based on selected regions of an image. The insertion is depending on the homogeneity of each part of the image. To determine the extent of homogeneity, there are two criteria: Mean and standard deviation are applied for the intensity of the image. Medium frequency bands of APDCBT in image pixel blocks are used to hold the embedded watermark. Then, the transform is inversed to obtain the resultant watermarked image. The robustness of APDCBT against watermark removal attacks is tested and the experimental results showed the superiority of the APDCBT over traditional DCT in the watermark embedding system. More specifically, when the LSB reset attack applied. For instance, when the contrast adjustment attack applied, the average of normalized cross-correlation (NCC) values of extracted watermark images with the original watermark, it was 0.992 for the proposed method. This is a promised result, if it is compared with the NCC of the DCT method, which was 0.423. The proposed method can be used for copyright protection purpose.

Simulation of Performance Comparison between Discrete Wavelet Transform and Discrete Cosine Transform Methods for Radar Signal Processing in High-Noise Area

Detection of low signal and determination target locations are tremendously important in the radar system. Performance of radar can be enhanced by improving the signal-to-noise ratio of the received signal at the receiver. In this paper, we demonstrate an algorithm in radar signal processing, due to extract the signal target in the place of noise. This algorithm is based on Discrete Cosines Transform (DCT) and Discrete Wavelet Transform (DWT) methods. DWT signal processing analyses and comparatives with mother wavelet Haar, Daubechies-12, Coiflet-5 and Symlet-8. In addition, DCT signal processing analyses and comparatives with the same window function and uses to restrict the signal. Window function has influenced the signal resolution in frequency domain. Window functions that are used in this research are Rectangular, Hamming, Hanning and Dolph-Chebyshev. The results of the simulation and analysis show that mother wavelet with DWT, wavelet Daubechies-12 and Symlet-8 gives the best performance while mother wavelet Haar gives a bad performance. Wavelet Daubechies-12 gives the biggest signal to noise ratio that is 32.0603 dB. Mother wavelet Symlet-8 gives 32.6589 dB. Mother wavelet Haar gives 14.6692 dB. Testing DCT window function, window Dolph-Chebyshev gives the best performance, which gives the best separation of the signal. Analysis of signal reflection that accepted by radar shows the result that DWT is giving better performance than DCT in breaking of noise.

A coarse-to-fine copy-move image forgery detection method based on discrete cosine transform

<span>Copy-move forgery is a type of image forgery where one part of an image is copied and pasted in other regions of the same image, and it is one of the most common image forgeries to conceal some information in the original image. Discrete Cosine Transform (DCT) is one of the detection techniques which the detection rate relies intensely on the size of block used. Small block size is known for its ability to detect fine cloned objects, but the drawback is it produces too many false positive and requires high execution time. In this research, a method to overcome the weaknesses of using small block size by applying the coarse-to-fine approach with the two-tier process is proposed. The proposed method is evaluated on fifteen forged images on the CoMoFoD dataset. The results demonstrated that the proposed method is able to achieve high precision and recall rate of over 90% as well as improves the computation time by reducing the overall duration of forgery detection up to 73% compared to the traditional DCT method using small block size. Therefore, these findings validate that the proposed method offers a trade-off between accuracy and runtime.</span>

Computer Based Method to Automatically Identify the Regions of Intracerebral Haemorrhage on Diffusion Weighted Magnetic Resonance Images

In recent times several methods for automated diagnostic systems have been proposed to overcome the problems faced due to large number of patients, and the necessity of having high accuracy when dealing with a human life. Traditional way of examining the Diffusion Weighted (DW) brain images of the subjects with Intracerebral Haemorrhage (ICH) involves the inspection of specific features, by a human observer. Present work makes an effort to develop a computer based method to automatically identify the regions of ICH on DW brain images of the ICH subjects, and thereby help in transforming the conventional qualitative investigative criteria into a quantitative feature classification problem. In this direction feature extraction techniques, namely Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are employed to provide description of the significant properties of the DW brain images. Subsequently, K-Nearest Neighbor (KNN) method of image classification is employed to analyze the properties of the extracted features. The maximum classification efficiency of the KNN classifier for the correct output classification of the ICH subjects using DCT method is obtained as 71.50% for k value = 1.00, and using DWT method is obtained as 90.00% for k value = 9.00, respectively. Results imply that the technique proposed in the present work could positively be helpful in the fast diagnosis of the ICH subjects, even in the absence of a medical expert.

Computer Based Method to Automatically Identify the Regions of Intracerebral Haemorrhage on Diffusion Weighted Magnetic Resonance Images

In recent times several methods for automated diagnostic systems have been proposed to overcome the problems faced due to large number of patients, and the necessity of having high accuracy when dealing with a human life. Traditional way of examining the Diffusion Weighted (DW) brain images of the subjects with Intracerebral Haemorrhage (ICH) involves the inspection of specific features, by a human observer. Present work makes an effort to develop a computer based method to automatically identify the regions of ICH on DW brain images of the ICH subjects, and thereby help in transforming the conventional qualitative investigative criteria into a quantitative feature classification problem. In this direction feature extraction techniques, namely Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are employed to provide description of the significant properties of the DW brain images. Subsequently, K-Nearest Neighbor (KNN) method of image classification is employed to analyze the properties of the extracted features. The maximum classification efficiency of the KNN classifier for the correct output classification of the ICH subjects using DCT method is obtained as 71.50% for k value = 1.00, and using DWT method is obtained as 90.00% for k value = 9.00, respectively. Results imply that the technique proposed in the present work could positively be helpful in the fast diagnosis of the ICH subjects, even in the absence of a medical expert.

A robust DCT-2DLDA watermark for color images

A blind watermarking algorithm is proposed, which is based on the Discrete Cosine Transform (DCT) method. It uses Two-Dimensional Linear Discriminant Analysis (2DLDA) watermark scheme for copyright protection. During the embedding process, the color image is converted into the YIQ color space. The quadrature chrominance component is transformed into frequency domain and it uses DCT method. Then, two binary watermarks for reference and logo are added to particular bits of the AC coefficients. During the extraction process, the logo watermark is extracted using matrix-based 2DLDA based on DCT method. By embedding the training data and using a matrix-based 2DLDA scheme, we do not need all the embedded information. The numbers of training samples for each class are small; the training covariance matrices are directly computed from 2D image samples by using 2DLDA. This ensures the efficient and the robustness of watermark extraction. Experimental results demonstrate that the differences between the watermarked image and the original image are indistinguishable. The proposed method is effectively resist common image processing attacks.

Digital image watermarking method based on DCT and fractal encoding

With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly using DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.

A Novel HBE-MCM-Based Multiplier Architecture for 8-Point DCT Structure

The Discrete Cosine Transform (DCT) structure plays a significant role in the signal processing applications such as image and video processing applications. In the traditional hardware design, the 8-point DCT architecture contains more number of logical slices in it. Also, it consists of number of multipliers to update the weight. This leads to huge area consumption and power dissipation in that architecture. To mitigate the conventional drawbacks, this paper presents a novel Hierarchical-based Expression (HBE)-Multiple Constant Multiplication (MCM)-based multiplier architecture design for the 8-point DCT structure used in the video CODEC applications. The proposed work involves modified data path architecture and Floating Point Processing Element (FPPE) architecture. Our proposed design of the multipliers and DCT architecture requires minimum number of components when compared to the traditional DCT method. The HBE-MCM-based multiplier architecture includes shifters and adders. The number of Flip-Flops (FFs) and Look Up Tables (LUTs) used in the proposed architecture is reduced. The power consumption is reduced due to the reduction in the size of the components. This design is synthesized in VERILOG code language and implemented in the Field Programmable Gate Array (FPGA). The performance of the proposed architecture is evaluated by comparing it with traditional DCT architecture in terms of the Number of FFs, Number of LUTs, area, power, delay and speed.

Digital Image Watermarking Algorithm Using the Intermediate Frequency

Digital image watermarking is one of the proposed solutions for copyright protection of multimedia data. This technique is better than Digital Signatures and other methods because it does not increase overhead. Watermarking adds the additional requirement of robustness. To improve the robustness of digital image watermarking method based on the image frequency, this paper adopts the intermediate frequency to embed the watermarking and proposes an digital image watermarking algorithm based on robust principal component analysis (RPCA) and discrete cosine transform (DCT). Firstly, the high frequency part and the low frequency part of the image are extracted by the RPCA algorithm. Because the high frequency part has complex statistical characteristics, this paper processes the high frequency part with 8×8 DCT method to obtain intermediate frequency coefficients and then the watermarking information is embedded into the obtained intermediate frequency coefficients. The experimental results show that the proposed algorithm leads to satisfactory robustness to the attacks of impulse noise and cropping.

Image Compression using DCT upon Various Quantization

Discrete cosine transform (DCT) is a widely compression technique for converting an image into elementary frequency components. However, level of quality and compression is desired, scalar multiples of the JPEG standard quantization may be used. In this paper, DCT method was applied to compress image under various level of quality. Different quantization matrices of DCT’s coefficients are used to improve level of quality and compression ratio of JPEG image.

Numerical Simulation of Phase Separation Kinetic of Polymer Solutions Using the Spectral Discrete Cosine Transform Method

ABSTRACTA spectral discrete cosine transform (DCT) method was used to solve numerically the spatio-temporal nonlinear Cahn-Hilliard equation for a temperature-induced phase separation process of a polymer solution. The properties of a high molecular weight polystyrene solution were used in the model to reflect the behavior of a real world polymer system. Based on the value of the initial concentration, three different final morphologies of the phase separated system, granular, interconnected, and microcellular, were identified. It was shown that the initial concentration is the main factor to determine the high rate of the phase separation when the phase separation time is the primary desired parameter. The degree of phase separation, as a quantitative measure of the phase separation process, indicates the rate and amount of separated polymer material for practical applications. The model is capable of providing quantitative information of morphology evolution during phase separation processes for microstructure control purposes. The structure factor profile was extracted as a theoretical output that enables comparison with scattering experiments observations as the finger-print of phase separation morphological evolution. It is shown that the DCT method is a very suitable and feasible method for solution of the nonlinear partial differential equation of phase separation kinetics.