Discrete Cosine Transform Coefficients Research Articles

Robust Adaptive Steganography Based on Dither Modulation and Modification With Re-Compression

Traditional adaptive steganography is a technique used for covert communication with high security, but it is invalid in the case of stego images are sent to legal receivers over networks which is lossy, such as JPEG compression of channels. To deal with such problem, robust adaptive steganography is proposed to enable the receiver to extract secret messages from the damaged stego images. Previous works utilize reverse engineering and compression-resistant domain constructing to implement robust adaptive steganography. In this paper, we adopt modification with re-compression scheme to improve the robustness of stego sequences in stego images. To balance security and robustness, we move the embedding domain to the low frequency region of DCT (Discrete Cosine Transform) coefficients to improve the security of robust adaptive steganography. In addition, we add additional check codes to further reduce the average extraction error rate based on the framework of E-DMAS (Enhancing Dither Modulation based robust Adaptive Steganography). Compared with GMAS (Generalized dither Modulation based robust Adaptive Steganography) and E-DMAS, experiment results show that our scheme can achieve strong robustness and improve the security of robust adaptive steganography greatly when the channel quality factor is known.

Desynchronization Attacks Resilient Watermarking Method Based on Frequency Singular Value Coefficient Modification

Desynchronization is a very challenging type of attack in audio watermarking. The traditional singular value decomposition (SVD) based audio watermarking methods embed the watermark information by modifying the singular value of individual segment, which have little resistance against desynchronization attacks. In this paper, we propose a novel frequency singular value coefficient (FSVC) feature, which reflects the ratio between the singular values of two consecutive segments and is insensitive to desynchronization attacks, to carry the watermark bits. To our best knowledge, it is the first time that the ratio between singular values is employed for audio watermarking. In the proposed method, the discrete cosine transform (DCT) is performed on two consecutive segments of the host audio signal and SVD is applied to the DCT coefficients of the mid frequency band of each segment to extract the FSVC. Then the watermark bits are embedded by adjusting the values of the FSVC. The watermark embedding procedure is optimized to minimize the perceptual quality degradation and an error buffer is created to enhance the robustness. As a result, the proposed method can achieve a much higher embedding capacity than the existing methods tackling desynchronization attacks. The impact of desynchronization and common signal processing attacks on the proposed watermarking method is mathematically modeled, and the effectiveness of the proposed method against these attacks is theoretically and experimentally validated.

Segmental DCT Coefficient Reversal Based Anti-Collusion Audio Fingerprinting Mechanism

Collusion attacks are challenging to tackle in audio fingerprinting. A new direction to resist collusion attacks is to degrade the perceptual quality of the colluded files so that these files cannot be reused. The existing method in this direction has low embedding capacity and limited anti-collusion performance when the number of colluders is odd. In this letter, we present an anti-collusion mechanism that has a higher embedding capacity and can significantly degrade the perceptual quality of the colluded files regardless of the number of colluders. In the proposed mechanism, we first segment the host audio file into frames and perform the discrete cosine transform (DCT) on each frame. Then multiple fingerprint bits are embedded into each frame by reversing the DCT coefficients of the corresponding frequency band. As a result, when a collusion attack occurs, our proposed embedding mechanism can introduce perceptibly annoying differences between frames in the colluded file, which leads to severe perceptual quality degradation. Theoretical analysis and experimental results validate the superiority of the proposed anti-collusion mechanism.

Real-Time Full-Chip Thermal Tracking: A Post-Silicon, Machine Learning Perspective

This article presents a novel approach to real-time tracking of full-chip heatmaps for off-the-shelf microprocessors based on machine-learning. The proposed post-silicon approach, named RealMaps, only uses the existing temperature sensors and workload-independent utilization information. RealMaps does not require any knowledge of the proprietary design or manufacturing process-specific details of the chip. Consequently, the methods presented in this work can be implemented by either the original chip manufacturer or a third party alike. The approach involves offline acquisition of spatial heatmaps using a thermal imaging setup. To build the dynamic thermal model, a temporal-aware long-short-term-memory neutral network is trained with system-level features as inputs. 2D discrete cosine transformation (DCT) is performed on the heatmaps so that they can be expressed with just a few dominant DCT coefficients. This allows the model to be built to estimate just the dominant spatial features of the heatmaps, rather than the entire heatmap images, making it significantly more efficient. Experimental results from two commercial chips show that RealMaps can estimate the full-chip heatmaps with 0.9C and 1.2C root-mean-square-error respectively and take only 0.4ms for each inference. Compared to the state of the art pre-silicon approach, RealMaps shows similar accuracy, but with much less computational cost.

Perceptual Image Quality Assessment Technique for Color Images based on HSI Colour Space

A new full-reference Perceptual Image Quality Assessment (PIQA) technique based on Hue-Saturation-Intensity (HSI) transformation method for colour images is offered in this work. Fundamentally, it is combined with three approaches (colour transformation, histogram processing and Human Visual System (HVS) based weighting) and it uses the Discrete Cosine Transform (DCT) domain. The PIQA is composed of four phases. At the first phase, a colour image is transformed using the HSI because an important characteristic of eyes reaction to intensity of light and colour knowledge is used for quality assessment processes. All channels' DCT coefficients have been calculated at the second phase, because many specifications cannot be assessed in the spatial domain. At the third phase, histogram based quality assessment results are obtained by using histogram of each channel (Hue, Saturation and Intensity). These results are weighted for obtaining perceptual quality result taking into account the HVS specifications at the last phase because the human eye has different sensitivities to the intensity and the others (i.e., hue and saturation). Experimental outcomes about feasibility of the PIQA on test images under different deteriorations, both sensible by the HVS and with the same Peak Signal to Noise Ratio (PSNR) result are offered. The PIQA shows better performance in comparison to state-of-the-art techniques

Perceptual Image Quality Assessment Technique for Color Images based on HSI Colour Space

A new full-reference Perceptual Image Quality Assessment (PIQA) technique based on Hue-Saturation-Intensity (HSI) transformation method for colour images is offered in this work. Fundamentally, it is combined with three approaches (colour transformation, histogram processing and Human Visual System (HVS) based weighting) and it uses the Discrete Cosine Transform (DCT) domain. The PIQA is composed of four phases. At the first phase, a colour image is transformed using the HSI because an important characteristic of eyes reaction to intensity of light and colour knowledge is used for quality assessment processes. All channels' DCT coefficients have been calculated at the second phase, because many specifications cannot be assessed in the spatial domain. At the third phase, histogram based quality assessment results are obtained by using histogram of each channel (Hue, Saturation and Intensity). These results are weighted for obtaining perceptual quality result taking into account the HVS specifications at the last phase because the human eye has different sensitivities to the intensity and the others (i.e., hue and saturation). Experimental outcomes about feasibility of the PIQA on test images under different deteriorations, both sensible by the HVS and with the same Peak Signal to Noise Ratio (PSNR) result are offered. The PIQA shows better performance in comparison to state-of-the-art techniques

Mathematical model for shape description in DCT domain

This paper derives a mathematical relationship between the shape information of an object and their discrete cosine transform (DCT) coefficients. Here, each column of the object is put into a matrix. As the lengths of different columns in the object are different, the lengths of the columns of the matrix are reset to the maximum value among these lengths of the columns of the object and the rest elements in the columns of the matrix are set to zero. The mathematical relationship between the shape information of the object and the DCT coefficients is derived. By substituting the DCT coefficients in the derived model, the shape of the object can be obtained directly in the DCT domain. Since most of the images are coded in the DCT domain, the derived result can significantly improve the efficiency for identifying the objects.

Perceptual image hashing using transform domain noise resistant local binary pattern

A new Discrete Cosine Transform (DCT) domain Perceptual Image Hashing (PIH) scheme is proposed in this paper. PIH schemes are designed to extract a set of features from an image to form a compact representation that can be used for image integrity verification. A PIH scheme takes an image as the input, extracts its invariant features and constructs a fixed length output, which is called a hash value. The hash value generated by a PIH scheme is then used for image integrity verification. The basic requirement for any PIH scheme is its robustness to non-malicious distortions and discriminative ability to detect minute level of tampering. The feature extraction phase plays a major role in guaranteeing robustness and tamper detection ability of a PIH scheme. The proposed scheme fuses together the DCT and Noise Resistant Local Binary Pattern (NRLBP) to compute image hash. In this scheme, an input image is divided into non-overlapping blocks. Then, DCT of each non-overlapping block is computed to form a DCT based transformed image block. Subsequently, NRLBP is applied to calculate NRLBP histogram. Histograms of all the blocks are concatenated together to get a hash vector for a single image. It is observed that low frequency DCT coefficients inherently have quite high robustness against non-malicious distortions, hence the NRLBP features extracted from the low frequency DCT coefficients provide high robustness. Computational results exhibit that the proposed hashing scheme outperforms some of the existing hashing schemes as well as can detect localized tamper detection as small as 3% of the original image size and at the same time resilient against non-malicious distortions.

Time-Domain Blind ICI Compensation in Coherent Optical FBMC/OQAM System.

A blind discrete-cosine-transform-based phase noise compensation (BD-PNC) is proposed to compensate the inter-carrier-interference (ICI) in the coherent optical offset-quadrature amplitude modulation (OQAM)-based filter-bank multicarrier (CO-FBMC/OQAM) transmission system. Since the phase noise sample can be approximated by an expansion of the discrete cosine transform (DCT) in the time-domain, a time-domain compensation model is built for the transmission system. According to the model, phase noise compensation (PNC) depends only on its DCT coefficients. The common phase error (CPE) compensation is firstly performed for the received signal. After that, a pre-decision is made on a part of compensated signals with low decision error probability, and the pre-decision results are used as the estimated values of transmitted signals to calculate the DCT coefficients. Such a partial pre-decision process reduces not only decision error but also the complexity of the BD-PNC method while keeping almost the same performance as in the case of the pre-decision of all compensated signals. Numerical simulations are performed to evaluate the performance of the proposed scheme for a 30 GBaud CO-FBMC/OQAM system. The simulation results show that its bit error rate (BER) performance is improved by more than one order of magnitude through the mitigation of the ICI in comparison with the traditional blind PNC scheme only aiming for CPE compensation.

An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

JPEG-based compression is the most widely used image compression algorithm. Previously, JPEG-XT-based work focused on 16-bit depth high-dynamic satellite infrared images, but not on medical images. In this study, we represent an optimized JPEG-XT method (OPT_JPEG-XT) that better compresses 16-bit depth medical images by amplifying (N times) discrete cosine transform (DCT) coefficients. The results show that the small integers and the first two decimal portions of DCT coefficients play important roles in the compression of medical images. By using the appropriate N and number of decimal portions (NDP), OPT_JPEG-XT could realize lossless compression of medical images. Regarding upper and lower 8-bit subimages, the upper subimages have more important roles in the improvement of OPT_JPEG-XT performance than the lower subimages; lower subimages could occupy over 90% sizes of the entire encoding files. Thus, OPT_JPEG-XT could save about 60% of storage space with high PSNR (peak signal-noise-ratio, over 100) and low MSE (mean-square-error, less than 0.08) by decreasing the compression efficiency of lower subimages. Compared to the conventional JPEG-XT and JPEG 2000, OPT_JPEG-XT can acquire a similar compression performance (PSNR > 100, MSE < 0.9, SSR (saving space rate, >60%) to JPEG 2000 when using N = 20 for lower subimages and lossless compression of upper subimages. OPT_JPEG-XT could obtain high SSR (about 90%, similar to traditional JPEG-XT) with with much smaller MSE (25 times lower than conventional JPEG-XT). Therefore, OPT_JPEG-XT could be developed a novel compression method that could realize the lossless and lossy compression of medical images.

Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging

ABSTRACTThis paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension (FD) after an appropriate image preprocessing stage that ensures a robust estimation process. In this study, we showed that within the frequency domain generated through discrete cosine transform (DCT), only a quarter of DCT coefficients are enough to characterize osteoporotic tissues. The algorithmic complexity of the developed approach is of the order of where N stands for the size of the image, which, in turn, likely yields important gain in terms of medication cost. We report a successful separation of healthy and pathological cases in term of both P − value (using statistical Wilcoxon rank sum test) and margin difference. A comparative statistical analysis has been performed using a publicly available database that contains a set of MRI and X‐Ray texture images of both healthy and osteoporotic bone tissues. The statistical results demonstrated the feasibility and accepted performance level of our fractal model‐based diagnosis to discriminate healthy and unhealthy trabecular bone tissues. The developed approach has been implemented on a medical device prototype.

A Modulo Function-Based Robust Asymmetric Variable Data Hiding Using DCT

This work presents a new asymmetric data hiding technique that hides a variable number of secret message bits in the discrete cosine transform (DCT) coefficients of a cover image using a modular distance technique. Prior to data hiding, the proposed framework transforms a cover image from a spatial domain to various frequency coefficients using DCT. The DCT coefficients are arranged in two groups: one with low-frequency coefficient, and the other with the medium and high-frequency coefficients. The medium and higher frequency coefficients are processed for variable data hiding asymmetrically. The proposed technique hides variable sets of secret information bits in different coefficients. The variation in hidden secret information is maintained using a key developed based on the modulo of distance of a coefficient from the reference point. The same key is also used to retrieve the confidential information at the receiver ends. The results reveal that the presented framework does not create any visually significant distortion, and thus the hidden information does not attract the human visual system (HVS). The technique also results in high data hiding efficiency.

A robust JPEG compression detector for image forensics

Identification of JPEG compressed images saved in uncompressed format (JPEG-U images) is an important issue in forensic analysis. The state-of-the-art JPEG compression detection methods fail to identify such images when subjected to post-processing/anti-forensic operations. In this paper, we propose a novel JPEG compression detector which is robust to post-processing and anti-forensic operations. The detector is based on the difference in the discrete cosine transform (DCT) coefficient distributions in the ac subbands of uncompressed images and JPEG-U images. We show theoretically and empirically that the probability of subband DCT coefficients which lie in the interval (−0.5,0.5) is significantly different for a JPEG-U and the corresponding uncompressed image. This difference is exploited to derive a detection statistic which is compared with a threshold to detect JPEG-U images. The detector makes use of calibration, a technique used in steganalysis, to obtain the detection statistic. The experimental results show that the proposed detector significantly outperforms the state-of-the-art detectors, especially in the presence of post-processing and anti-forensic operations.

Well logs reconstruction by DCT based IPRM and genetic algorithm

Hydrocarbon production from a particular layer requires careful identification of the stratigraphic boundaries and choosing the right perforation against the face of oil-bearing intervals.Excessive perforations may cause unwanted water production from adjacent layers. Nowadays, clustering of well log data through various methods such as self-organized maps (SOM) helps to find hydrocarbon intervals from other layers. SOM is a type of artificial neural network, which uses the non-monitored learning of the multi-dimensional logging observations to create a 2D clustering map. However, the noisy response data greatly reduces accuracy of the methods by increasing correlation of different data groups. Although spectral such as discrete wavelet transform (DWT) can reduce correlation and reconstruct the logs that no longer fluctuate as before, they have two major limitations. First, responses smooth the edge-shape behaviors in the log curve while preserving them is essential for recognizing the stratification changes. The second limitation is the generation of spurious oscillations in the reconstructed signals at the vicinity of the edges, known as the Gibbs phenomena. At the present study to overcome these problems, we use, for the first time, the inverse polynomial reconstruction method (IPRM) based on discrete cosine transform (DCT) to attenuate the random noise by projecting the data first on the orthogonal bases derived from Gegenbauer polynomials and then on cosine space. The de-noising level depends on the minimum description length (MDL) criterion that minimizes the distance of all the corresponding coefficients between the original and reconstructed signals during 100 iterations. However, in the proposed method, we use a genetic algorithm to decide quickly and optimally the coefficients of DCT and Gegenbauer required in real data reconstruction related to one of the oil fields in southwestern Iran. The results show that this preprocessing procedure compared to DCT and DWT, increases the success rate of the SOM method for oil-bearing zone detection based on evidence such as core drilling and perforation data. At the same time, the computational time is much shorter than the usual IPRM. The optimal perforation subsequent to this study may cut unwanted water production from adjacent layers. Such water with radioactive nature, in addition to causing environmental problems, greatly increases the cost of extraction operations due to the need to install surface-separating equipment.

Adaptive video data hiding with low bit-rate growth based on texture selection and ternary syndrome-trellis coding

Data hiding aims to embed a secret payload into a cover object without introducing significant degradation of the cover. The resulting object containing hidden information, typically also called stego, will not arouse obvious suspicion from the monitor. A number of data hiding systems have been designed for digital images and only a few focus on video sequences. Actually, video is quite desirable for data hiding due to its large capacity for carrying a payload. It motivates us to present an adaptive data hiding scheme to video sequences in this paper. In the proposed scheme, the non-zero quantized Discrete Cosine Transform (QDCT) coefficients of intra-frames are used to embed the secret payload based on ternary syndrome-trellis coding (STC) equipped with a well-designed distortion function. In order to realize data hiding with low bit-rate growth, the non-zero QDCT coefficients in complex texture regions with high amplitude are further exploited for data embedding. Experimental results have shown that, comparing with a part of related works, the proposed work provides high embedding capacity and lower bit-rate growth. And, the video quality after data hiding can be kept with a high level. In addition, the proposed work does not expose suspicious histogram distribution and block characteristics, which has shown the superiority and applicability of the proposed work.

A novel multi‐image visually meaningful encryption algorithm based on compressive sensing and Schur decomposition

AbstractImage encryption algorithm is an important telecommunication technology for protecting personal security. In this article, we propose a novel multi‐image visual encryption algorithm based on compressive sensing (CS) and Schur decomposition. First, multiple images are sparsed by a discrete wavelet transform, and the compressed images are obtained by zigzag confusion and CS. Second, these compressed images are combined according to random sequences and scrambled again to obtain the secret image. Third, the carrier image is subjected to a two‐level lifting wavelet transform where the low‐frequency and high‐frequency subbands are selected to perform a discrete cosine transform (DCT). Fourth, Schur decomposition is employed on the secret image to calculate an upper triangular matrix and a unitary matrix (orthogonal matrix) to be embedded into the DCT coefficients of the previous low‐frequency and high‐frequency subbands, respectively. With an inverse transformation, the final visually meaningful encrypted image is achieved. Here, the initial position of the zigzag confusion is related to the plain image, which can effectively resist known‐plaintext and chosen‐plaintext attacks. The simulation results demonstrate that the proposed algorithm has high security and good imperceptibility.

Reversible data hiding based on a modified difference expansion for H.264/AVC video streams

Reversible Data Hiding (RDH), also referred to as invertible or lossless data hiding, has gradually received much attention from the research community in recent years. In this paper, a RDH scheme for H.264/AVC videos is proposed and it is based on a novel way to expand the difference of a pair of Quantized Discrete Cosine Transform (QDCT) coefficients. The secret data is embedded into the QDCT coefficients of 4 × 4 luminance blocks which selected according to two conditions to achieve good visual quality. In every embeddable 4 × 4 blocks each to- be-embedded information bit is embedded into the selected coefficient-pair. All the selected coefficient-pairs are changed by the proposed difference expanding strategy, which causes low distortion on visual quality. Experimental results have demonstrated that the proposed scheme obtains a little degradation of visual quality and keeps the variation low in bit-rate increase.

A new low-complexity approximate DCT for image and video compression

ABSTRACT In this paper, a new approximate discrete cosine transform (DCT) architecture for an H.265/HEVC video compression codec is proposed. In order to reduce the computational complexity, a configuration of basic elements by 0, ±0.5, ±1 is proposed to improve the DCT coefficient performance. Next, we derive different DCT coefficients for different transform block sizes based on DCT symmetry properties for H.265. Experimental results show that the proposed DCT can increase by 4 dB with a peak signal-to-noise ratio (PSNR) on average compared with general approximate DCT approaches. In comparison with recent approximation DCTs, the proposed architecture cannot only reduce the computational complexity but can also obtain good quality in PSNR. The proposed configurable DCT architecture successfully reduces approximately 40% of the hardware cost for multiple transform blocks.

Parameterized Strain Estimation for Vascular Ultrasound Elastography With Sparse Representation.

Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-compacted property of discrete cosine transform (DCT) coefficients, thereby allowing to parameterize displacement and strain fields with truncated DCT coefficients. The derivation of affine strain components (axial and lateral strains and shears) was reformulated into solving truncated DCT coefficients and then reconstructed with them. Moreover, an analytical solution was derived to reduce estimation time. With simulations, the SMSE reduced estimation errors by up to 50% compared with the state-of-the-art window-based Lagrangian speckle model estimator (LSME). The SMSE was also proven to be more robust than the LSME against global and local noise. For in vitro and in vivo tests, residual strains assessing cumulated errors with the SMSE were 2 to 3 times lower than with the LSME. Regarding computation efficiency, the processing time of the SMSE was reduced by 4 to 25 times compared with the LSME, according to simulations, in vitro and in vivo results. Finally, phantom studies demonstrated the enhanced spatial resolution of the proposed SMSE algorithm against LSME.

Discrete cosine transform for parameter space reduction in linear and non-linear AVA inversions

Geophysical inversions estimate subsurface physical parameters from the acquired data and because of the large number of model unknowns, it is common practice reparametrizing the parameter space to reduce the dimension of the problem. This strategy could be particularly useful to decrease the computational complexity of non-linear inverse problems solved through an iterative sampling procedure. However, part of the information in the original parameter space is lost in the reduced space and for this reason the model parameterization must always constitute a compromise between model resolution and model uncertainty. In this work, we use the Discrete Cosine Transform (DCT) to reparametrize linear and non-linear elastic amplitude versus angle (AVA) inversions cast into a Bayesian setting. In this framework the unknown parameters become the series of coefficients associated to the DCT base functions. We first run linear AVA inversions to exactly quantify the trade-off between model resolution and posterior uncertainties with and without the model reduction. Then, we employ the DCT to reparametrize non-linear AVA inversions numerically solved through the Differential Evolution Markov Chain and the Hamiltonian Monte Carlo algorithm. To draw general conclusions about the benefits provided by the DCT reparameterization of AVA inversion, we focus the attention on synthetic data examples in which the true models have been derived from actual well log data. The linear inversions demonstrate that the same level of model accuracy, model resolution, and data fitting can be achieved by employing a number of DCT coefficients much lower than the number of model parameters spanning the unreduced space. The non-linear inversions illustrate that an optimal model compression (a compression that guarantees optimal resolution and accurate uncertainty estimations) guarantees faster convergences toward a stable posterior distribution and reduces the burn-in period and the computational cost of the sampling procedure.