Dimensional Discrete Cosine Transform Research Articles

Underwater Optical Image Coding for Marine Health Monitoring Based on DCT

Introduction: Optical imaging in the underwater environment to monitor marine objects is now a hot topic of research which can be used for environmental healthcare systems through the underwater ecosystem. Among different areas of research, image coding techniques are widely applied to compress data for reliable communications. One of the challenges faced during the underwater communications is having a low bit rate in acoustic links, particularly while doing imaging in deep waters (in this condition, light needed for imaging is provided by a power supply). Materials and Methods: Two Dimensional-Discrete Cosine Transform (2D-DCT) is the main technique that we want to use for image compression, to test two different patch sizes in 2D-DCT to study the patch size effect on the quality of compression, execution time and preservation ability of high-frequency information in edges. Results: The results clearly show that a larger patch size can always be better in terms of computational complexity, quality of coded images and also edge preservation when we use DCT for the compression process. Discussion and Conclusion: b-image block in image compression (in terms of similarity and complexity), however, the use of an edge preservation factor is a new finding for our research. On the other hand, using the largest patch size is not a general approach for all image processing applications, because some studies have shown that smaller patch may be more effective for some other applications.

Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification

ABSTRACTBand selection is an effective means of reducing the dimensionality of the hyperspectral image by selecting the most informative and distinctive bands. Bands are usually selected by adopting information theoretic measures, such as, the information entropy or mutual information. However, directly using these information theoretic measures for selection of informative bands is not enough. Therefore, in this paper, the Three Dimensional Discrete Cosine Transform (3D DCT)-based information entropy is used for band selection from a high-dimensional data space. DCT has excellent energy compaction properties for highly correlated images such as hyperspectral images, which reduces the complexity of the separation significantly. First, DCT is applied to extract the complementary features from the raw hyperspectral cube. The weighted information entropy is introduced for measurement of the information about the discrimination ability of each band and reduction of the redundancy among them. An efficient search strategy is then used to select the optimal bands set. The selected bands are fed to a Support Vector Machine (SVM) to carry out the classification task. The proposed band selection approach is tested using three standard hyperspectral datasets such as, Indian Pines, Pavia University and Salinas dataset. The experimental results demonstrate the promising discriminant capability of the DCT features. The proposed method achieves maximum overall classification accuracy of about 84.61% for Indian Pines, 92.83% for Pavia University, and 94.14% for Salinas dataset. When compared to the other competitive band selection methods, the proposed approach shows the significant increase in the overall classification accuracy, when the number of selected bands ranges from 20 to 50.

Complexity Analysis and Accuracy of Image Recovery Based on Signal Transformation Algorithms

In this paper we compare and analyze the complexity of three functions: Fast Fourier transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), used in image transformation. The purpose of all the algorithms is to shift the signal from space or time domain to frequency domain for de-noising or compression. We compare the simulated process time of both one and two dimensional FFT, DCT and DWT (Symlet and Debauches 1) using image and speech signal. The process time is found lowest for FFT and highest for DWT, provided its basis function governs the process time and DCT provide the moderate result. Finally the quality of compressed image under the three mathematical functions are compared, where DWT is found as the best and FFT yields worst result.

Wavelet Transform Domain for Deep Image Compression Using High Frequency Sub-Band Prediction

This procedure demonstrates a Two Dimensional Discrete Cosine Transform (2D-DCT) system with quantization and zigzag layout. The JPEG picture compression approach uses this process as the foundation and main channel. Utilizing the 2D- DCT property, the calculation divides the overall process into compression and decompression. Even though the data in the images remained the same, the pixel was reduced predicated on the quantization and zigzag process. Division operation is used throughout the quantization process. As a result, this procedure minimizes data loss during decompression. The theoretical research offered in this study offers some fresh perspectives on how local variation behaves when JPEG compression is applied. Additionally, it might be utilised in a few aspects of image processing and analysis, including picture enhancement, image quality evaluation, and image filtering. MATLAB is used for this project, and improved vector quantization is used. Using the compression ratio, MSE, and PSNR, we can produce compressed pictures.

Comparison of Discrete Cosine Transforms (DCT), Discrete Fourier Transforms (DFT), and Discrete Wavelet Transforms (DWT) in Digital Image Watermarking

Digital Image Watermarking is used recently to secure the image by embedding another digital image. It is typically used to identify ownership of the copyright of the signal. Frequency domain transformation methods used widely in Digital Image Compression and Digital Image Watermarking. They reduce the weakness of classics digital image watermarking such as Least Significant Bit (LSB) methods which is more noise-tolerant. Popular transformation method used are Two Dimensional Discrete Cosine Transform (2D DCT), Two Dimensional Discrete Fourier Transforms (2D DFT), and Two Dimensional Discrete Wavelet Transform (2D DWT). This paper will show the comparison result of those three transformation method. The experiments are comparison analysis of image watermark quality using Peak Signal to Noise Ratio (PSNR), color converting, image resizing, image optical scanning and the noise-tolerant of the image watermarked by giving Gaussian noise.

Watermarking-based Color Image Authentication with Detection and Recovery Capability

In this paper a watermarking-based color image authentication with detection and recovery capability is proposed, in which a Halftone image is used as an approximated version of the luminance channel (Y) and a coded version of the coefficients of the Two Dimensional Discrete Cosine Transform of the chrominance channels (Cb, Cr) of the YCbCr color space are used as watermark signal. The luminance and chrominance information is embedded into the sub-bands of lowest frequency of the Integer Wavelet Transform of the original luminance channel using Quantization Index Modulation Dither Modulation. The luminance information in embedded into the LL sub-band meanwhile the chrominance information is embedded into the LH and HL sub-bands. Besides, a Multilayer Perceptron Neural Network is used as inverse halftoning to increase the quality of the recovered image. The experimental results show the effectiveness of the proposed method compared with some published before.

FPGA Implementation of Approximate 2D Discrete Cosine Transforms

Discrete cosine transform (DCT) is frequently used in image and video signal processing due to its high energy compaction property. Humans are able to perceive and identify the information from slightly erroneous images. It is enough to produce approximate outputs rather than absolute outputs which in turn reduce the circuit complexity. Numbers of applications like image and video processing need higher dimensional DCT algorithms. So the existing architectures of one dimensional (1D) approximate DCTs are reviewed and extended to two dimensional (2D) approximate DCTs. Approximate 2D multiplier-free DCT architectures are coded in Verilog, simulated in Modelsim to evaluate the correctness, synthesized to evaluate the performance and implemented in virtexE Field Programmable Gate Array (FPGA) kit. A comparative analysis of approximate 2D DCT architectures is carried out in terms of speed and area.

Age Estimation Based on AAM and 2D-DCT Features of Facial Images

This paper proposes a novel age estimation method - Global and Local feAture based Age estiMation (GLAAM) - relying on global and local features of facial images. Global features are obtained with Active Appearance Models (AAM). Local features are extracted with regional 2D-DCT (2- dimensional Discrete Cosine Transform) of normalized facial images. GLAAM consists of the following modules: face normalization, global feature extraction with AAM, local feature extraction with 2D-DCT, dimensionality reduction by means of Principal Component Analysis (PCA) and age estimation with multiple linear regression. Experiments have shown that GLAAM outperforms many methods previously applied to the FG-NET database.

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).

Two-stage neural network regression of eye location in face images

Automatic eye localization is a crucial part of many computer vision algorithms for processing face images. Some of the existing algorithms can be very accurate, albeit at the cost of computational complexity. In this paper, a new solution to the problem of automatic eye localization is proposed. Eye localization is posed as a nonlinear regression problem solved by two feed-forward multilayer perceptrons (MLP) working in a cascade. The input feature vector of the first network is constructed from coefficients of a two dimensional discrete cosine transform(DCT) of a face image. The second network generates corrections based on small image patches. Feature extraction and neural network prediction have known and efficient implementations, thus the entire procedure can be very fast. The paper hints at the neural network structure and the procedure for generating artificial training samples from a low number of face images. In terms of accuracy, the method is comparable to state-of-the-art techniques; however it is based on numerical procedures that could be highly optimized (fast Fourier transform and matrix multiplication).

Forensic Face Sketch Recognition Using Computer Vision

Now - a - days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each - other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However studies say that hand - drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victims sketches on behalf of the descripti on provided by an eyewitness. Sometimes suspects used special mask to hide some common features of faces like nose, eyes, lips, face - color etc. but the outliner features of face biometrics one could never hide. In this work, I concentrated on some specific facial geometric feature which could be used to calculate some ratios of similarities from the template photograph database against the forensic sketches. This paper describes the design of a system for forensic face sketch recognition by a computer visi on approach like Two - Dimensional Discrete Cosine Transform (2D - DCT) and the Self - Organizing Map (SOM) Neural Network simulated in MATLAB.

Face Recognition Using Two Dimensional Discrete Cosine Transform, Linear Discriminant Analysis And K Nearest Neighbor Classifier

In this paper, a new Face Recognition method based on Two Dimensional Discrete Cosine Transform with Linear Discriminant Analysis (LDA) and K Nearest neighbours (KNN) classifier is proposed. This method consists of three steps, i) Transformation of images from special to frequency domain using Two dimensional discrete cosine transform ii) Feature extraction using Linear Discriminant Analysis and iii) classification using K Nearest Neighbour classifier. Linear Disceminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Two Dimensional Discrete Cosine transform and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. K Nearest Neighbour classifier gives fast and accurate classification of face images that makes this method useful in online applications. Evaluation was performed on two face data bases. First database of 400 face images from AT&T face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers. The main advantage of this method is its high speed processing capability and low computational requirements in terms of both speed and memory utilizations. DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.767

Performance Analysis of Text-Independent Speaker Identification System

In this paper, the text independent speaker identification system is implemented and analyzed using the speaker specific features extracted by applying image processing techniques. The speech is converted into spectrogram, an efficient representation of the speech signal in the form of pattern. Radon Transform (RT) and Discrete Cosine Transform (DCT) are applied on the spectrogram to extract the features. Radon Transform is applied for eight orientations on the acoustic characteristics of the spectrogram. After applying RT, the two dimensional DCT has been applied on Radon projections yields low dimensional feature vectors. The performance of the algorithm has been evaluated with our own created BCS database using sound forge version 5.0. The effect of number of Radon projections and DCT coefficients are analyzed. The demonstration program is coded by MATLAB V 7.0.4. The recognition rate of this algorithm is 96% on BCS database

Performance Analysis of Text-Independent Speaker Identification System

In this paper, the text independent speaker identification system is implemented and analyzed using the speaker specific features extracted by applying image processing techniques. The speech is converted into spectrogram, an efficient representation of the speech signal in the form of pattern. Radon Transform (RT) and Discrete Cosine Transform (DCT) are applied on the spectrogram to extract the features. Radon Transform is applied for eight orientations on the acoustic characteristics of the spectrogram. After applying RT, the two dimensional DCT has been applied on Radon projections yields low dimensional feature vectors. The performance of the algorithm has been evaluated with our own created BCS database using sound forge version 5.0. The effect of number of Radon projections and DCT coefficients are analyzed. The demonstration program is coded by MATLAB V 7.0.4. The recognition rate of this algorithm is 96% on BCS database.

A novel technique for image steganography based on Block-DCT and Huffman Encoding

Image steganography is the art of hiding information into a cover image. This paper presents a novel technique for Image steganography based on Block-DCT, where DCT is used to transform original image (cover image) blocks from spatial domain to frequency domain. Firstly a gray level image of size M x N is divided into no joint 8 x 8 blocks and a two dimensional Discrete Cosine Transform (2-d DCT) is performed on each of the P = MN / 64 blocks. Then Huffman encoding is also performed on the secret messages/images before embedding and each bit of Huffman code of secret message/image is embedded in the frequency domain by altering the least significant bit of each of the DCT coefficients of cover image blocks. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret message/image cannot be extracted without knowing decoding rules and Huffman table.

Prime factor algorithm for multidimensional discrete cosine transform

A prime factor fast algorithm is proposed for the computation of the multidimensional forward and inverse discrete cosine transform (DCT). By using an example of a two-dimensional (2-D) DCT, it shows that an r-dimensional DCT can be obtained from a 2r dimensional DCT with a post-processing stage, efficient method for input/output mapping is reported to substantially reduce the computational overhead associated with the prime factor algorithm.

Fast Discrete Cosine Transform via Computation of Moments

Discrete cosine transform (DCT) is widely used in signal processing. This paper presents a novel approach to perform DCT. DCT is expressed in terms of discrete moments via triangle function transforms and later Taylor series expansion. From this, a fast systolic array for computing moments is converted to compute DCT with only a few multiplications and without any cosine evaluations. The systolic array has advantages of pipelinability, regularity, modularity, local connectivity and scalability, thus making it to be very suitable for VLSI implementation. We provide an estimate of the realizability of our array in a 0.5 μm CMOS technology and comparisons with other methods. The execution time of the systolic array is only O(N \log_2 N/\log_2 \log_2N) in computing 1D N-point DCT if N is sufficiently large. The approach is also applicable to multiple dimensional DCT and DCT inverses.

An experimental digital VCR for consumer use

An experimental digital VCR (DVCR hereinafter) was developed. The DVCR has two new technologies for reduction of tape consumption. One is a bit rate reduction technology of a component video signal down to around 25 Mbps and the other is a high density recording technology. The bit rate reduction technology is based on an 8/spl times/8/2/spl times/4/spl times/8 two dimensional DCT (Discrete Cosine Transform) and a VLC (Variable Length Coding) that completes over 5 macro blocks. Editing, trick plays and invisible error concealments also have been realized by this bit rate reduction while keeping the playback picture quality very high. The high density recording technology is based on ME tape and an ATF (Automatic Track Finding) system. A track pitch of 10 /spl mu/m and a bit length on tape of 0.25 /spl mu/m have been realized. The possibility of a higher linear recording density has been confirmed through theoretical analysis, simulations and experiments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An experimental home-use digital VCR with three dimensional DCT and superimposed error correction coding

An experimental digital VCR system for home use is described. A high-quality moving picture is obtained by a newly developed bit rate reduction technology, which is based on subband and three-dimensional DCT (discrete cosine transform) coding. A high-density magnetic recording method which adopts a 12-to-15 modulation scheme and superimposed error correction coding enable sufficient recording time with a small cassette. Component video signals with a ration of 4:2:2 were compressed to about the ratio of 1/7, keeping a high quality, using the subband and 3-D-DCT coding method. >

A Copyright- and Privacy-Protected Diabetic Retinopathy Diagnosis Network

This paper proposes a copyright- and privacyprotected diabetic retinopathy (DR) diagnosis network. In the network, DR lesions are automatically detected from a fundus image by firstly estimating non-uniform illumination of the image, and then the lesions are detected from the balanced image by using level-set evolution without re-initialization. The lesions are subsequently marked by using contours. The lesion-marked fundus image is subsequently shared for intra or inter hospital network diagnosis with copyright and privacy protection. Watermarking technique is used for image copyright protection, and visual encryption is used for privacy protection. Sign scrambling of two dimensional (2D) discrete cosine transform (DCT) and one dimensional (1D) DCT is proposed for lesion-marked fundus image encryption. The proposed encryption methods are compared with other transform-based encryption methods, i.e., discrete Fourier transform (DFT) amplitude-only images (AOIs), DCT AOIs, and JPEG 2000-based discrete wavelet transform (DWT) sign scrambling which were proposed for image trading system. Since the encryption is done after DR diagnosis, contours used for DR marking must also be visually encrypted. The proposed encryption methods are effective for strong-edge images that are suitable for lesion-marked fundus images, while random sign-based JPEG 2000, DFT AOIs, and DCT AOIs encrypt the images imperfectly. Moreover, the proposed methods are better in terms of image quality. In addition, watermarking performance and compression performance are confirmed by experiments.