Coefficients Of Discrete Cosine Research Articles

Frequency aware face hallucination generative adversarial network with semantic structural constraint

In this paper, we address the issue of face hallucination. Most current face hallucination methods rely on two-dimensional facial priors to generate high resolution face images from low resolution face images. These methods are only capable of assimilating global information into the generated image. Still there exist some inherent problems in these methods, such as, local features, subtle structural details and depth information are missing in final output image. This work proposes a generative adversarial network (GAN) based novel progressive face hallucination (FH) network to address these issues present among current methods. The generator of the proposed model comprises of FH network and two sub-networks, assisting FH network to generate high resolution images. The first sub-network leverages on explicitly adding high frequency components into the model. To explicitly encode the high frequency components, an auto encoder is proposed to generate high resolution coefficients of discrete cosine transform (DCT). To add three dimensional parametric information into the network, second sub-network is proposed. This network uses a shape model of 3D morphable models (3DMM) to add structural constraint to the FH network. Extensive experimentation evaluation show the usefulness of proposed architecture in the form of state-of-the-art quantitative results.

A blind robust color image watermarking method based on discrete wavelet transform and discrete cosine transform using grayscale watermark image

AbstractWith the rapid development of internet technology, digital images are easily accessed and tampered by unauthorized users. Therefore, copyright of color images has become an important challenge in nowadays. Robust color image watermarking methods have emerged to provide copyright. However, robust watermarking methods must satisfy the requirements such as robustness, imperceptibility, capacity, and security. Therefore, researchers are focused on methods using grayscale watermark instead of the binary watermark to increase capacity. Grayscale watermark based robust color image watermarking methods are mostly semi‐blind and non‐blind. However, these methods have limited utilization areas since the original image, original watermark, or side information is required. To address these drawbacks, we propose a blind robust color image watermarking method using grayscale watermark. In the proposed watermark embedding procedure, one‐level discrete wavelet transform is performed on the Cb color component. Then, scrambled watermark pixels are hidden into coefficients of discrete cosine transform (DCT) matrices of LL subband by using dividing‐based new watermark embedding formula. In the watermark extracting procedure, watermark pixels are extracted from coefficients of DCT matrices by using dividing‐based extracting formula. The performance is demonstrated against various attacks and experimental results indicate that the presented method ensures robustness while preserving imperceptibility.

Visual Saliency via Multiscale Analysis in Frequency Domain and Its Applications to Ship Detection in Optical Satellite Images.

This article proposes a bottom-up visual saliency model that uses the wavelet transform to conduct multiscale analysis and computation in the frequency domain. First, we compute the multiscale magnitude spectra by performing a wavelet transform to decompose the magnitude spectrum of the discrete cosine coefficients of an input image. Next, we obtain multiple saliency maps of different spatial scales through an inverse transformation from the frequency domain to the spatial domain, which utilizes the discrete cosine magnitude spectra after multiscale wavelet decomposition. Then, we employ an evaluation function to automatically select the two best multiscale saliency maps. A final saliency map is generated via an adaptive integration of the two selected multiscale saliency maps. The proposed model is fast, efficient, and can simultaneously detect salient regions or objects of different sizes. It outperforms state-of-the-art bottom-up saliency approaches in the experiments of psychophysical consistency, eye fixation prediction, and saliency detection for natural images. In addition, the proposed model is applied to automatic ship detection in optical satellite images. Ship detection tests on satellite data of visual optical spectrum not only demonstrate our saliency model's effectiveness in detecting small and large salient targets but also verify its robustness against various sea background disturbances.

Strategizing secured image storing and efficient image retrieval through a new cloud framework

Nowadays, a major problem in faster page loading through optimizing websites is the absence of having images in their intended sizes. Accordingly, while loading a webpage, most of the time overhead pertains to image-related tasks such as image loading and resizing, which can be optimized substantially through the pre-availability of smaller-size images. Therefore, in this study, we propose a strategy to enable faster and efficient image retrieval from a cloud via the necessary pre-processing of images beyond conventional online processing. We also extend the abilities of cloud file sharing from the conventional “only storing images” to pre-processing along with security reinforcing. Here, we perform the first task of resizing images to several dimensions for covering diversified remote user devices available nowadays. Then, we perform encoding and encryption of images using the P-Fibonacci transform of Discrete Cosine Coefficients (PFCC) algorithm. Afterward, we resize images using the Bicubic interpolation method to both JPEG and Progressive-JPEG (PJPEG) formats by adding a new middleware named iBuck for faster and smooth retrieval. We leverage necessary algorithms and image types after careful evaluation of their performances in our intended framework. Our evaluation covers both objective and subjective evaluations including QoE metrics such as MSE, SSIM, etc. Furthermore, we conduct detailed experimentation over real setups to evaluate the performance of our implemented middleware in a cloud file sharing environment with both custom and standard data sets. Our evaluation reveals substantial performance improvement using our proposed framework compared to that using conventional alternatives.

A watermarking scheme for source authentication, ownership identification, tamper detection and restoration for color medical images

This paper proposes a blind watermarking scheme that has ability of confidentiality, source authentication, ownership identification, integrity, tampering detection and restoration of the medical images. It embeds the hospital logo, electronic patient record and perceptual hash value of region of interest in the mid-frequency coefficients of discrete cosine transform of the region of non-interest of medical image. The region of interest of the image is restored against attacks including the desynchronization attacks and impulse noise attack, and the severity of tampering due to any attack is measured by comparing the difference between the original perceptual hash value of the region of interest and the extracted perceptual hash value of region of interest of watermarked image. Restoration is done that tampers the region of interest of the images. Number of medical image watermarking schemes that show resilient to many different types of singular and hybrid attacks is very few. The proposed method has inbuilt restoration schemes against different attacks such as rotation, scaling, translation, shearing, horizontal reflection, vertical reflection and impulse noise attacks. Comparison with the latest and state-of-art medical image watermarking schemes shows that the performance of the proposed method is superior to other methods.

Application of Machine Learning Methods for Biometric Identification by Iris

The paper is devoted to the application of machine learning methods for biometric identification of the iris. for biometric identification of a person by iris recognition. In order to investigate the possibilities of machine learning for the iris recognition, the UBIRIS database was used in this work. A distinctive feature of this database is that the task of identification is complicated by the registration of images in non-ideal conditions. This approach is aimed at developing algorithms for automatic iris recognition under limited conditions for obtaining high-quality images, remote registration, noise, and in the absence of cooperation by the subject. Glare from light sources, differences in lighting conditions, closure of part of the eye by the eyelids complicate the recognition process due to the significant fluctuation of features for different images of the iris of one person. The purpose of this work is to identify and compare the informative features of the iris obtained in different coordinate bases, as well as to choose the classification method that provides the highest accuracy in the identification of the person on the iris. Comparison of the use in iris recognition such features as the coefficients of two-dimensional discrete Fourier transform, the coefficients of discrete cosine transform, as well as the approximation coefficients of the 3rd and 4th levels of two-dimensional wavelet transform is considered. The accuracy of a number of classifiers using these feature sets is determined. Based on this, a combination of features and classifiers are proposed that provides the highest accuracy of person identification based on the iris recognition. The highest classification accuracy of the iris recognition was 98.5% using the approximation coefficients of the 4th level of two-dimensional wavelet decomposition and a classifier implementing the linear discriminant method. The use of two-dimensional discrete Fourier transform coefficients for iris recognition yielded 97.5% correctly identified individuals in the case of machine learning method based on the k-nearest neighbor ensemble. This algorithm also made it possible to obtain maximum classification accuracy (94.5%) for the coefficients of the discrete cosine transformation. The use of wavelet coefficients can significantly reduce the dimensionality of the data for classification compared to the use of Fourier transform coefficients or discrete cosine transform. Applying of vertical, horizontal and diagonal wavelet coefficients of detail as features for iris recognition gives lower accuracy compared to the use of wavelet coefficients of approximation of the same level.

СТЕГАНОГРАФИЧЕСКОЕ ВСТРАИВАНИЕ ДОПОЛНИТЕЛЬНЫХ ДАННЫХ В СНИМКИ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ С ПОМОЩЬЮ МЕТОДА QIM С ПЕРЕМЕННЫМ ШАГОМ КВАНТОВАНИЯ В ЧАСТОТНОЙ ОБЛАСТИ

Актуальность. Одно из направлений обработки цифровых изображений, в том числе снимков дистанционного зондирования Земли, связано со встраиванием в них дополнительной информации различного назначения. Встраивание дополнительной информации в цифровое изображение приводит к искажению естественной модели цифрового изображения, а также к возможному возникновению визуальных артефактов. В случае снимков дистанционного зондирования Земли такие искажения могут повлечь, например, искажение границ объектов, в результате чего дальнейший анализ снимков приведёт к некорректным результатам. Поэтому актуальными являются исследования, направленные на поиск новых способов уменьшения искажений, вызванных встраиванием дополнительной информации. Цель: повышение качества стеганографического встраивания информации в коэффициенты дискретного косинусного преобразования снимков дистанционного зондирования Земли за счёт разработки улучшенного алгоритма встраивания, основанного на методе QIM и обеспечивающего корректировку искажений естественной модели изображения в частотной области. Объекты: алгоритмы стеганографического встраивания информации в коэффициенты дискретного косинусного преобразования цифровых изображений. Методы: стеганографический метод QIM, методы математической статистики, вычислительные эксперименты. Результаты. Предлагается новый подход к минимизации искажений естественной модели цифрового изображения в области дискретного косинусного преобразования, основанный на изменении шага квантования в зависимости от локальных характеристик изображения в частотной области. Результатом работы является улучшенный алгоритм встраивания информации в область дискретного косинусного преобразования снимков дистанционного зондирования Земли, объединяющий данный подход с подходом к обеспечению безошибочного извлечения встроенной информации, предложенным авторами ранее. Результаты экспериментов показывают, что разработанный алгоритм наряду с обеспечением безошибочности извлечения позволяет существенно уменьшить искажения, вносимые в частотные коэффициенты, и обеспечить статистическую неразличимость исходных изображений и стегоизображений в 75 % случаев.

An asymmetric and optimized encryption method to protect the confidentiality of 3D mesh model

3D models are widely used in computer graphics, design and manufacture engineering, art animation and entertainment. With the universal of acquisition equipment and sensors, a huge number of 3D models are generated, which are becoming the major source of engineering data. How to preserve the privacy of the 3D models is a challenge issue. In this paper, an asymmetric and optimized encryption method is presented to protect the 3D mesh models. Firstly, we propose an asymmetric encryption method for 3D mesh models to overcome the drawbacks of traditional symmetric encryption. The primary benefit is that our approach can enhance the security of the key. Secondly, we extend the typically asymmetric encryption algorithm from integer domain to float domain. In our method, we present a normalization function to map the float DC (Discrete Cosine) coefficients to integer domain. Thirdly, considering that the shape error and encryption/decryption computation cost are contradictory in the normalization mapping, we formulate the contradiction as a multi-objective optimization problem. And then, we propose a multi-objective solution to find an optimized mapping range for encryption/decryption efficiently. Furthermore, benefiting from the proposed asymmetric encryption framework, we continue to put forward a method to check the integrity of the encrypted 3D mesh model, in which the digest is encrypted twice to generate digital signature more safely. The proposed method has been tested on 3D mesh models from Stanford university and other sources to demonstrate the effect of the proposed encryption method and optimization mechanism.

Image denoising with morphology- and size-adaptive block-matching transform domain filtering

BM3D is a state-of-the-art image denoising method. Its denoised results in the regions with strong edges can often be better than in the regions with smooth or weak edges, due to more accurate block-matching for the strong-edge regions. So using adaptive block sizes on different image regions may result in better image denoising. Based on these observations, in this paper, we first partition each image into regions belonging to one of the three morphological components, i.e., contour, texture, and smooth components, according to the regional energy of alternating current (AC) coefficients of discrete cosine transform (DCT). Then, we can adaptively determine the block size for each morphological component. Specifically, we use the smallest block size for the contour components, the medium block size for the texture components, and the largest block size for the smooth components. To better preserve image details, we also use a multi-stage strategy to implement image denoising, where every stage is similar to the BM3D method, except using adaptive sizes and different transform dimensions. Experimental results show that our proposed algorithm can achieve higher PSNR and MSSIM values than the BM3D method, and also better visual quality of denoised images than by the BM3D method and some other existing state-of-the-art methods.

An Algorithm of Digital Image Watermarking Based on Turbo Code and Particle Swarm Optimization

To protect the copyright information of digital images, this paper presents a combination of Particle Swarm Optimization and turbo code of the image watermarking algorithm. Firstly, in order to enhance the security of watermark information, binary image is encrypted by Aronld transform and error correction coding technology. Based on the block of the host image, according to human visual properties, make discrete cosine transform to the block with larger texture complexity, and embed the watermark information in the low frequency coefficients of discrete cosine transform with the embedding intensity of watermarking searched by PSO. The simulation results show that the algorithm not only improves the security of the watermarking, but also has better robustness and invisibility of the watermarking.

Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

Fire hazards occurring recently in the world lead to the need of designing accurate fire detection systems in order to save human lives. The newest innovations continue to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications like the adaboost classifier which is well known for its strength in rigid objects detection from images. This paper presents a Fire Detection System (FDS) with an algorithm that works side by side with the adaboost classifier to determine the presence of fire in an image taken by a normal web camera (webcam), in order to decrease the false alarms in an indoor scene. The images are first preprocessed and their selected discrete cosine coefficients are kept for analysis to get better coefficients that will be fed to a neural network for classification and results are compared to a statistical approach used in combination with binary background mask (BBM) and a wavelet-based model of fire’s frequency signature(WMF) to test its accuracy

DCT Coefficients Modelling for Image Watermarking

It is important to select the best well-modelled coefficients of discrete cosine transform (DCT ) to satisfy the requirements of watermarking algorithm. In this paper, we proved that the low ac DCT coefficients are well modelled by using the generalized gamma distribution. A new estimator based on moment method was proposed to estimate their parameters, and the goodness-of-fit (χ2)is used to prove its effectiveness. The modelled coefficients were chosen to perform the watermark embedding process. To increase the security of our scheme, the chaotic maps have been used. Model of the probability edensity functions for the DCT watermarked images is derived, and the detector is developed based on the maximum likelihood. Simulation results proved that the proposed method has better imperceptibility, highly sensitive to the key and robust against JPEG attack when the moment method is used. With our method ewe can still detect the watermark for JPEG compression when the compression ratio is over 28.5 for all attacked watermarked images.

Modes of protection of video data from unauthorized access

The paper reviews existing methods of protection of vid­eo. A consequence of the mass distribution of video technology has become illegal copying and viewing of video data - a problem of protection of video. In particu­lar, video data can be distributed to copyright infringe­ment (piracy), and these may refer unauthorized competi­tors or hackers to obtain sensitive information (espio­nage). The simplest approach to the protection of video data - is die use of classical encryption schemes open or secret key. Image data file is encrypted and then transmit­ted over the insecure communications channel or record­ed on unprotected media. This method provides a very high degree of protection of video data is achieved by a high resistance used for protection cipher. Often, howev­er, the amount of encrypted data over the text and audio data even considerably more, which requires significant computing resources to such encryption of data. This tends to limit the possibility of using classical encryption in areas such as interactive and cable TV. Users of these services should have a powerful system capable of real­time, without delays decoding, and then decode the re­ceived data. Since the main standard used to encode and compress video data format is MPEG, then most of the methods of protection designed specifically for this for­mat. They use features and coding structure of MPEG to reduce the computational resources to protect the video data. One way to protect the first data in the MPEG algo­rithm was permutation «zigzag». Its essence consists in reading the quantized coefficients of discrete cosine trans­form method is not «zigzag» for subsequent encoding as defined format, randomly. Different methods of protec­tion vidiodannyh applied to telecommunications systems real-time.

A zero-watermarking algorithm of stereoscopic image based on hyperchaotic system

In order to protect the copyright of stereoscopic images in the condition of the image quality, a zero-watermarking stereoscopic image algorithm is presented based on hyperchaotic system. In the proposed algorithm, disparity zero-watermark is constructed according to the stability of disparity between low frequency bands of wavelet decomposition of the left and the right views and direct coefficients of discrete cosine transform; in the process of zero-watermark construction, the zero-watermark position mapping according to the features of the sensitivity of hyperchaotic discrete system initial value, ampleness of parameter key space and complexity of dynamic behavior, enhances the security of algorithm. The relationship between security and watermark capacity is also analyzed. Experimental results show that the proposed algorithm is strong robust to noise, filtering, compression, and the shearing of asymmetrical and symmetrical attacks.

Robust image watermarking via geometrically invariant feature points and image normalisation

The robustness of watermarks to geometric attacks is viewed as an issue of great importance. Indeed, it constitutes one of the most challenging design requirements for watermarks. This study proposes a robust image watermarking scheme using visually significant feature points and image normalisation. In order to tackle the issue of geometric distortions, the authors adopt a feature extraction method based on end-stopped wavelets to extract significant geometry preserving feature points, which are shown to be robust against various types of common signal processing and geometric attacks. These feature points can be used as synchronisation marks between watermark embedding and detection. The watermark is embedded into non-overlapping normalised circular images, which are determined by feature points. Rotation invariance is achieved via image normalisation. The watermark embedding process is performed by modifying low-frequency coefficients of discrete cosine transform blocks, which are randomly selected using a secret key. Moreover, the security of the scheme is further guaranteed by an image-dependent key. The proposed scheme is blind as the original image is not required at the watermark detection. Experimental results show that the proposed scheme is robust against geometric attacks as well as common signal processing attacks and outperforms related techniques found in the literature.

A New Watermarking Algorithm Based on Entropy Concept

This paper presents an alternative semi-blind watermarking algorithm in gray scale images. The new watermarking system changes the coefficients of discrete cosine transform in homogeneous manner. This algorithm is based on the modification of the base using the concept of the norm or the distance. Experimental results introduced at the end of this paper, demonstrates the robustness of the proposed strategy against different attacks.

Compressed image reproduction based on block decomposition

A compressed image reproduction scheme is proposed by properly decomposing and manipulating the coefficients of discrete cosine transform (DCT) directly in the compressed domain. The basic idea of the proposed approach is to decompose each DCT block into several sub-blocks and to adjust the brightness and detail components of a given image for compressing dynamic range and enhancing contrast. Image reproduction based on the subblock decomposition can be done more precisely than any approach based on the normal block-sized approach. First, DCT coefficients of each block are decomposed into several sub-blocks. Next each sub-block's coefficients are separated into brightness and detail components, and treated differently according to content analysis. Then, the enhanced coefficients are projected on the constraint sets to avoid some artefacts, and are composed back to the original order. The main advantages of the proposed algorithm are that (i) it can enhance the dynamic range and details without affecting the compressibility of the given image since it operates directly in the compressed domain, and (ii) it does not boost blocking artefacts around big edges without any further processing. In order to evaluate the proposed scheme, several base-line approaches are described and compared using enhancement quality measures.

Novel hybrid classified vector quantization using discrete cosine transform for image compression

We present a novel image compression technique using a classified vector Quantizer and singular value decomposition for the efficient representation of still images. The proposed method is called hybrid classified vector quantization. It involves a simple but efficient classifier-based gradient method in the spatial domain, which employs only one threshold to determine the class of the input image block, and uses three AC coefficients of discrete cosine transform coefficients to determine the orientation of the block without employing any threshold. The proposed technique is benchmarked with each of the standard vector quantizers generated using the k-means algorithm, standard classified vector quantizer schemes, and JPEG-2000. Simulation results indicate that the proposed approach alleviates edge degradation and can reconstruct good visual quality images with higher peak signal-to-noise ratio than the benchmarked techniques, or be competitive with them.

Ensemble of multiple Palmprint representation

In this work, a new approach for personal authentication using palm image is presented. We design three ensembles of matchers which employ different feature representation schemes of the images: discrete cosine coefficients; invariant local binary patterns; Gabor filters. Each ensemble is obtained by varying the features used to train their matchers. Experimental results confirm that the three methods give complementary information which has been exploited by fusion rules. Finally, we combine our Palm based method system with other biometric characteristics that can be extracted from the hand (middle finger, ring finger, hand geometry), obtaining a further improvement of the performance.

Discrete cosine transform coding with gain/shape vector quantizers for picture signals

AbstractA gain/shape vector quantizer for orthogonal transform domain has been designed based on a previous paper [11] on a gain/shape vector quantizer for a spherically symmetric random source. Such a design method is used in the design of a discrete cosine transform encoder with gain/shape vector quantizer. This encoder is similar to that described in [7] except in the following: (i) it has a gain/shape codebook; and (ii) its normalized coefficients of discrete cosine transform are modeled as having spherically symmetric distribution. The S/N rendition of this encoder is 1.0 – 2.5 dB better than that of the DCT coding scheme with Max‐type scalar quantizer at the same bit rate. Furthermore, in spite of its similarity to the conventinoal discrete cosine transform coding scheme with vector quantizer [7], the encoder introduced in this paper delivers higher S/N using the same number of multiplications for the quantization of normalized coefficients, and/or the same amount of memory for the codebook.