Two-dimensional Discrete Cosine Transform Research Articles

Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data.

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from the dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when compared with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.

Texture Classification using Angular and Radial Bins in Transformed Domain

Texture is generally recognized as fundamental to perceptions. There is no precise definition or characterization available in practice. Texture recognition has many applications in areas such as medical image analysis, remote sensing, and robotic vision. Various approaches such as statistical, structural, and spectral have been suggested in the literature. In this paper we propose a method for texture feature extraction. We transform the image into a two-dimensional Discrete Cosine Transform (DCT) and extract features using the ring and wedge bins in the DCT plane. These features are based on texture properties such as coarseness, smoothness, graininess, and directivity of the texture pattern in the image. We develop a model to classify texture images using extracted features. We use three classifiers: the Decision Tree, Support Vector Machine (SVM), and Logarithmic Regression (LR). To test our approach, we use Brodatz texture image data set consisting of 111 images of different texture patterns. Classification results such as accuracy and F-score obtained from the three classifiers are presented in the paper.

New image blind watermarking method based on two-dimensional discrete cosine transform

In this paper, a new image blind watermarking method based on two-dimensional Discrete Cosine Transform (2D-DCT) is presented to realize copyright protection of color image. At first, 2D-DCT is performed on the selected image blocks, and some fixed coefficients in the middle frequency are selected as embedding position of watermark. Then, the watermark embedding and extraction procedure are completed by modifying the relationship of size between the selected middle-frequency coefficients with the proposed rules. The advantages of the presented algorithm include the following two points: 1) the relationship of size between the selected middle-frequency coefficients of 2D-DCT is used to fulfill the process of watermark embedding and extraction; 2) the presented algorithm uses color digital image as carrier image and watermark image, which is not same as most watermarking techniques. In recent years, most of the watermarking algorithms used gray or binary image as watermark. In this presented algorithm, all the digital carrier images are chosen from two public standard image databases, i.e. CVG-UGR and USC-SIPI. A series of simulation results prove that this presented algorithm not only satisfies the invisibility of watermarking algorithm, but also makes good performance of robustness, security and embedding capacity.

Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

Innovative Savonius rotors evolved by genetic algorithm based on 2D-DCT encoding

The Savonius rotor is mainly known by its design simplicity, low cost, easy installation, good starting ability, relatively low operating speed and independent to wind directions. Over the last few decades, although many investigations around the world have reported performance gains of the Savonius rotor, none of the available techniques is capable of automatic generating innovative Savonius rotor geometries other than predefined models such as semicircular or its variations. This study aims to investigate the geometry optimization ability of evolution algorithms based on two-dimensional discrete cosine transformation (2D-DCT) through numerical simulations. By considering the cross-sectional area of a Savonius rotor as a picture, 2D-DCT has been employed to compress and encode the cross-sectional geometry into genomes which will be evolved by Genetic Algorithms to maximize their average torque coefficient. According to numerical simulation, the proposed technique improved the efficiency by 13.77% at tip speed ratio (TSR) 1.0 and 21.11% at TSR 0.0838, which discovered impressive and thought promoting innovative geometries automatically.

Block Energy Based Visual Features Using Histogram Of Oriented Gradient For Bimodal Hindi Speech Recognition

In this paper, new set of visual speech feature based on Histogram of Oriented Gradient (HOG) is proposed to improve the robustness of bimodal Hindi speech recognition. For extracting the visual features, energy per block using HOG is calculated by finding the gradient magnitude of the pixel in the cell for both x and y direction form the region of interest (ROI). The advantage of proposed scheme is that it has reduced the dimensionality of visual features vectors which can retain the full information of the lip region. For comparative study, four sets of visual feature; Set A (Two-Dimensional Discrete Cosine Transform feature (2D-DCT)), Set B (Two-Dimensional Discrete Wavelet Transform followed by DCT (2D-DWT-DCT)), Set C (Static-HOG) and Set D (Dynamic-HOG) are extracted from AMUAV corpus. Standard Mel Frequency Cepstral coefficients (MFCC) followed by static and dynamic (MFCC) features were used as baseline features. The maximum improvement in WRA (%) of 12.73% is reported over baseline features using proposed features sets.

Audio-visual word prominence detection from clean and noisy speech

In this paper we investigate the audio-visual processing of linguistic prosody, more precisely the detection of word prominence, and examine how the additional visual information can be used to increase the robustness when acoustic background noise is present. We evaluate the detection performance for each modality individually and perform experiments using feature and decision fusion. For the latter we also consider the adaptive fusion with fusion weights adjusted to the current acoustic noise level. Our experiments are based on a corpus with 11 English speakers which contains in addition to the speech signal also videos of the speakers’ heads. From the acoustic signal we extract features which are well known to capture word prominence like loudness, fundamental frequency and durational features. The analysis of the visual signal is based on features derived from the speaker’s rigid head movements and movements of the speaker’s mouth. We capture the rigid head movements by tracking the speaker’s nose. Via a two-dimensional Discrete Cosine Transform (DCT) calculated from the mouth region we represent the movements of the speaker’s mouth. The results show that the rigid head movements as well as movements inside the mouth region can be used to discriminate prominent from non-prominent words. The audio-only detection yields an Equal Error Rate (EER) averaged over all speakers of 13%. Based only on the visual features we obtain 20% of EER. When we combine the visual and the acoustic features we only see a small improvement compared to the audio-only detection for clean speech. To simulate background noise we added 4 different noise types at varying SNR levels to the acoustic stream. The results indicate that word prominence detection is quite robust against additional background noise. Even at a severe Signal to Noise Ratio (SNR) of −10 dB the EER only rises to 35%. Despite this the audio-visual fusion leads to notable improvements for the detection from noisy speech. We observe relative reductions of the EER of up to 79%.

A fast texture segmentation scheme based on active contours and discrete cosine transform

This paper presents a novel and efficient parametric active contour model based on the texture describing properties derived from coefficients of the two-dimensional Discrete Cosine Transform (2-D DCT) for segmenting texture objects against complex backgrounds. Block based 2-D DCT is applied in the local neighbourhood of all the contour control points and some randomly chosen object points. DCT coefficients in the horizontal, vertical and diagonal directions which represent the dominant textures are used to construct the histograms those exhibit different structures for different texture regions. Heterogeneity of these histograms corresponding to contour and object points is measured by Wasserstein distance metric. Finally, DC coefficient which represents the energy of the corresponding block modulated by the calculated Wasserstein distance is employed as the external energy of the proposed active contour model. Experiments on synthetic and natural texture images show the efficiency of our model in terms of accuracy and speed.

Denoising of sparse images in impulsive disturbance environment

The paper presents a method for denoising and reconstruction of sparse images based on a gradient-descent algorithm. It is assumed that the original (non-noisy) image is sparse in the two-dimensional Discrete Cosine Transform (2D-DCT) domain. It is also assumed that a number of image pixels is corrupted by a salt and pepper noise. In addition, we assume that there are pixels corrupted by a noise of any value. In this paper we introduce a method to find the positions of the corrupted pixels when the noise is not of the salt and pepper form. The proposed algorithm for noisy pixels detection and reconstruction works blindly. It does not require the knowledge about the positions of corrupted pixels. The only assumption is that the image is sparse and that the noise degrades this property. The advantage of this reconstruction algorithm is that we do not change the uncorrupted pixels in the process of the reconstruction, unlike common reconstruction methods. Corrupted pixels are detected and removed iteratively using the gradient of sparsity measure as a criterion for detection. After the corrupted pixels are detected and removed, the gradient algorithm is employed to reconstruct the image. The algorithm is tested on both grayscale and color images. Additionally, the case when both salt and pepper noise and a random noise, within the pixel values range, are combined is considered. The proposed method can be used without explicitly imposing the image sparsity in a strict sense. Quality of the reconstructed image is measured for different sparsity and noise levels using the structural similarity index, the mean absolute error, mean-square error and peak signal-to-noise ratio and compared to the traditional median filter and recent algorithms, one based on the total-variations reconstruction and a two-stage adaptive algorithm.

A Fast DCT Algorithm for Watermarking in Digital Signal Processor

Discrete cosine transform (DCT) has been an international standard in Joint Photographic Experts Group (JPEG) format to reduce the blocking effect in digital image compression. This paper proposes a fast discrete cosine transform (FDCT) algorithm that utilizes the energy compactness and matrix sparseness properties in frequency domain to achieve higher computation performance. For a JPEG image of8×8block size in spatial domain, the algorithm decomposes the two-dimensional (2D) DCT into one pair of one-dimensional (1D) DCTs with transform computation in only 24 multiplications. The 2D spatial data is a linear combination of the base image obtained by the outer product of the column and row vectors of cosine functions so that inverse DCT is as efficient. Implementation of the FDCT algorithm shows that embedding a watermark image of 32 × 32 block pixel size in a 256 × 256 digital image can be completed in only 0.24 seconds and the extraction of watermark by inverse transform is within 0.21 seconds. The proposed FDCT algorithm is shown more efficient than many previous works in computation.

Area and Power Efficient VLSI Architecture for Two Dimensional 16-point Modified Gate Diffusion Input Discrete Cosine Transform

The two-dimensional (2D) Discrete Cosine Transform (DCT) is used widely in image and video processing systems. The perception of human visualization permits us to design approximate rather than exact DCT. In this paper, we propose a digital implementation of 16-point approximate 2D DCT architecture based on one-dimensional (1D) DCT and Modified Gate Diffusion Input (MGDI) technique. The 8-point 1D Approximate DCT architecture requires only 12 additions for realization in digital VLSI. Additions can be performed using the proposed 8 transistor (8T) MGDI Full Adder which reduces 2 transistors than the existing 10 transistor (10T) MGDI Full Adder. The Approximate MGDI 2D DCT using 8T MGDI Full adders is simulated in Tanner SPICE for 0.18㎛ CMOS process technology at 100MHZ.The simulation result shows that 13.9% of area and 15.08 % of power is reduced in the 8-point approximate 2D DCT, 10.63 % of area and 15.48% of power is reduced in case of 16-point approximate 2D DCT using 8 Transistor MGDI Full Adder than 10 Transistor MGDI Full Adder. The proposed architecture enhances results in terms of hardware complexity, regularity and modularity with a little compromise in accuracy.

REML estimation with intrinsic Matérn dependence in the spatial linear mixed model

We present a new matrix-free residual maximum likelihood (REML) analysis for irregularly spaced spatial data, where observations usually represent average values over very small regions that are interpreted as points. The REML analysis is obtained after embedding the sampling locations in a fine scale rectangular lattice, treating unobserved sites as missing data. The spatial random fields considered here are based on fractional Laplacian differencing on the lattice and they are unique in approximating continuum intrinsic Matern dependence. Here, using the h-likelihood method, we derive REML estimating equations that allow for singular precision matrices, estimation of covariate effects, prediction of unobserved spatial effects and REML estimation of precision parameters as a solution to an explicit gamma non-linear model. Furthermore, we devise a sophisticated computational algorithm that enables us to achieve scalable matrix-free statistical computations. In particular, these matrix-free computations include the use of (1) the two-dimensional discrete cosine transformation that arises in the spectral decomposition of the precision matrix of our spatial random fields and that allows fast matrix-free matrix-vector multiplication, (2) a matrix-free pre-conditioned Lanczos algorithm that solves non-sparse matrix equations with linear constraints, (3) a matrix-free Hutchinson’s trace estimator that stochastically approximates the trace of a matrix, (4) a robust trust region method that always finds a local maximum of the non-concave residual log-likelihood function and (5) some preliminary computations of the log REML likelihood function based on Taylor series approximation. Using computer experiments, we provide further understanding on not just the number and values but also the basins of attraction of the local and global maxima of the REML function. This understanding significantly simplifies the problem of finding global maxima. We further demonstrate through computer experiments that our matrix-free REML estimators attain both efficiency and geostatistical inference, and surpass the widely used INLA methods in computational times. We provide an extensive application on mapping ground water arsenic concentration in Bangladesh, indicating numeric consistency of results and robustness of inference to changes of lattice spacing. The paper closes with some discussions that include computations in the stationary case, conditional simulations and matrix-free MCMC computations.

Comparative Study of Visual Feature for Bimodal Hindi Speech Recognition

Abstract In building speech recognition based applications, robustness to different noisy background condition is an important challenge. In this paper bimodal approach is proposed to improve the robustness of Hindi speech recognition system. Also an importance of different types of visual features is studied for audio visual automatic speech recognition (AVASR) system under diverse noisy audio conditions. Four sets of visual feature based on Two-Dimensional Discrete Cosine Transform feature (2D-DCT), Principal Component Analysis (PCA), Two-Dimensional Discrete Wavelet Transform followed by DCT (2D-DWT- DCT) and Two-Dimensional Discrete Wavelet Transform followed by PCA (2D-DWT-PCA) are reported. The audio features are extracted using Mel Frequency Cepstral coefficients (MFCC) followed by static and dynamic feature. Overall, 48 features, i.e. 39 audio features and 9 visual features are used for measuring the performance of the AVASR system. Also, the performance of the AVASR using noisy speech signal generated by using NOISEX database is evaluated for different Signal to Noise ratio (SNR: 30 dB to −10 dB) using Aligarh Muslim University Audio Visual (AMUAV) Hindi corpus. AMUAV corpus is Hindi continuous speech high quality audio visual databases of Hindi sentences spoken by different subjects.

Efficient Robust Speech Authentication Algorithm for Perceptual Hashing Based on Hilbert-Huang Transform

According to the resource-constrained of mobile communication terminal, the traditional broadband speech authentication algorithm cannot satisfy the authentication of a small amount of data, strong robustness and high e‐ciency, etc. This paper, using the short-time stable characteristics of speech signal, proposed an e‐cient robust speech authentication algorithm for perceptual hashing based on Hilbert-Huang Transform (HHT). Firstly, the algorithm obtains to the HHT coe‐cients of each speech frames and constitutes an HHT coe‐cients matrix after pre-processing, framing and adding window to speech signal. Then, the HHT coe‐cients matrix for two-dimensional Discrete Cosine Transform (DCT) generates DCT coe‐cients. It based on DCT coe‐cient characteristics to evaluate its entropy. Finally, a quantization step gets authentication perceptual hash sequence. Experimental results show that the algorithm, compared with the existing methods, has a good robustness and discrimination for content preserving operations, and the short authentication time. Also, it can meet the real-time requirement of the speech perceptual authentication.

Neural Network based Face Recognition System using Discrete Cosine Transform and Principal Component Analysis

This paper deals with the implementation of Neural Network based face recognition system. As we know that face recognition system is one of the biometric information processing which has speed up in the last few decades. The developed algorithm for the face recognition system originates an image based approach, which uses the TwoDimensional Discrete Cosine Transform (2D-DCT) to compress image, and then Self Organizing Map (SOM) Neural Network to recognize the face and its simulated in MATLAB. With the help of 2D-DCT the image vectors are extracted and these vectors sends to the neural network classifier which is developed using self organizing map, algorithm to recognize trained faces, faces with variations in expressions, changes of illumination, upto certain degrees. The alternate way of the same face recognition system is developed with the help of principle component analysis (PCA) instead of Two Dimensional Cosine Transform and Self-Organizing Map Neural Network to recognize the faces. In this proposed algorithm we use unsupervised single neural network as a classifier for both Two Dimensional Discrete Cosine Transform (2D-DCT) and Principal Component Analysis (PCA).

An image compression using using discrete cosine transforms and JPEG enconder

The proposed Multiresolution decomposition through wavelets transform of fractals coded images reveal strong relationships limit the frequency content .The research is includes other extensions of the wavelet transform and fractals are wavelet transform. And our work deals with deals with the JPEG Encoder for image compression upon comparing with Discrete cosine transform (DCT) is widely used in image processing, especially for compression. Some of the applications of two-dimensional DCT involve still image compression and compression of individual video frames.The JPEG standard has been an effective first solution to the standardization of image and the performance of DWT compression with DCT compression. And JPEG 2000 is a wavelet-based standard for the compression of still digital images The wavelet transform has emerged as a cutting edge technology, within the field of image compression. And Wavelet- based coding provides substantial improvements in picture quality at higher compression ratios.and also dicuss about of the proposed DCT based JPEG Encoder and Wavelet based JPEG Encoder.

Two-Dimensional Discrete Sine Transform and Discrete Cosine Transform Based on Two-Dimensional Multimode Interference Couplers

In this letter, all-optical two-dimensional discrete sine transform and discrete cosine transform (2DDST/DCT) are proposed to be realized on two-dimensional multimode interference (2DMMI) couplers. Similar to the one-dimensional case, the 2DDST/DCT requires only one 2DMMI coupler and a few phase shifters.

Estimation of human red blood cells size using light scattering images

In this paper, a novel method for the estimation of the human Red Blood Cell (RBC) size using light scattering images is presented. The information retrieval process includes, image normalization, a two-dimensional Discrete Cosine Transformation (DCT2) or Wavelet transformation (DWT2), and a Radial Basis Neural Network (RBF-NN) estimates the RBC geometrical properties. The proposed method is evaluated in both regression and identification tasks when three important geometrical properties of the human RBC are estimated using a database of 1575 simulated images generated with the boundary element method. The experimental setup consists of a light beam at 632.8 nm and moving RBCs in a thin glass and additive noise distortion is simulated using white Gaussian noise from 60 to 0 dB SNR. The regression and identification accuracy of actual RBC sizes is estimated using three feature sets, giving a mean error rate less than 1 percent of the actual RBC size, in case of noisy image data at 10 dB SNR or better, and more than 97 percent mean identification rate.

Motion-compensated DCT temporal filters for efficient spatio-temporal scalable video coding

Although most of the proposals for implementing motion-compensated temporal filtering (MCTF) schemes are based on the wavelet transform, in this paper, we propose an MCTF framework based on the discrete cosine transform (DCT). Using DCT decimation and interpolation, several temporal decomposition structures named motion-compensated DCT temporal filters (MCDCT-TF) are introduced. These structures are able to employ filters of any length with particular emphasis on 5/3 DCT and 7/4 DCT. The proposed MCDCT-TF and the two-dimensional (2D) DCT decimation technique are incorporated into H.264/AVC to provide spatio-temporal scalability. Compared with the current MCTF-based lifting schemes such as Haar, and 5/3 wavelet filters, simulation results show that the proposed MCDCT-TF utilizing longer tap DCT filters achieves a significant improvement in coding gain. The impact of odd/even group of frames, the decimation/interpolation ratios, and motion-compensated connectivity on the MCDCT-TF performance are also analyzed. Moreover, simulation results show that the performance of the presented scalable video coding is close to the single layer H.264/AVC and is slightly inferior to the temporal scalability supported in JSVM, the state-of-the-art scalable video coding standard, that gets its gain from Hierarchical B-pictures. However, our spatio-temporal coding scheme outperforms the spatio-temporal supported in JSVM even if it uses hierarchical B-pictures to improve its gain.

An efficient architecture for motion estimation and compensation in the transform domain

This paper describes a new architecture for discrete cosine transform (DCT)-based motion estimation and compensation. Previous methods do not take sufficient advantage of the sparseness of two-dimensional (2-D) DCT coefficients to reduce execution time. We first derive a recursion equation for transform domain motion estimation; we then use it to develop a wavefront array processor consisting of highly regular, parallel, and pipelined processing elements that more efficiently performs motion estimation. In addition, we show that the recursion equation enables motion predicted images with different frequency bands, for example, from the images with low-frequency components to the images with low- and high-frequency components. The wavefront array processor can reconfigure to different motion estimation algorithms, such as logarithmic search and three step search, without architectural modifications. These properties can be effectively used to reduce the energy required for video encoding and decoding. Simulation results on video sequences of different characteristics show that the proposed architecture achieves a significant reduction in computational complexity and processing time, with comparable performance to spatial domain approaches with respect to the peak signal to noise ratio (PSNR) and the compression ratio.