Discrete Cosine Transform Coefficients Research Articles

Robust Image Steganography: Hiding Messages in Frequency Coefficients

Steganography is a technique that hides secret messages into a public multimedia object without raising suspicion from third parties. However, most existing works cannot provide good robustness against lossy JPEG compression while maintaining a relatively large embedding capacity. This paper presents an end-to-end robust steganography system based on the invertible neural network (INN). Instead of hiding in the spatial domain, our method directly hides secret messages into the discrete cosine transform (DCT) coefficients of the cover image, which significantly improves the robustness and anti-steganalysis security. A mutual information loss is first proposed to constrain the flow of information in INN. Besides, a two-way fusion module (TWFM) is implemented, utilizing spatial and DCT domain features as auxiliary information to facilitate message extraction. These two designs aid in recovering secret messages from the DCT coefficients losslessly. Experimental results demonstrate that our method yields significantly lower error rates than other existing hiding methods. For example, our method achieves reliable extraction with 0 error rate for 1 bit per pixel (bpp) embedding payload; and under the JPEG compression with quality factor QF=10, the error rate of our method is about 22% lower than the state-of-the-art robust image hiding methods, which demonstrates remarkable robustness against JPEG compression.

TinyNeRF: Towards 100 x Compression of Voxel Radiance Fields

Voxel grid representation of 3D scene properties has been widely used to improve the training or rendering speed of the Neural Radiance Fields (NeRF) while at the same time achieving high synthesis quality. However, these methods accelerate the original NeRF at the expense of extra storage demand, which hinders their applications in many scenarios. To solve this limitation, we present TinyNeRF, a three-stage pipeline: frequency domain transformation, pruning and quantization that work together to reduce the storage demand of the voxel grids with little to no effects on their speed and synthesis quality. Based on the prior knowledge of visual signals sparsity in the frequency domain, we convert the original voxel grids in the frequency domain via block-wise discrete cosine transformation (DCT). Next, we apply pruning and quantization to enforce the DCT coefficients to be sparse and low-bit. Our method can be optimized from scratch in an end-to-end manner, and can typically compress the original models by 2 orders of magnitude with minimal sacrifice on speed and synthesis quality.

Robust Video Watermarking Algorithm based on DCT-SVD approach and Encryption

Sharing of digital media content over the internet is increasing everyday .Digital watermarking is a technique used to protect the intellectual property rights of multimedia content owners. In this paper, we propose a robust video watermarking scheme that utilizes Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) for embedding a watermark into video frames. The proposed method uses encryption to make the watermark more robust against malicious attacks. The encryption key is used to modify the watermark before it is embedded in the video frames. The modified watermark is then embedded in the DCT and SVD coefficients of the video frames. The experimental results show that the proposed method provides better robustness against various attacks such as compression, noise addition, and filtering, while maintaining good perceptual quality of the watermarked video. The proposed method also shows better resistance against geometric attacks such as cropping, rotation, and scaling. Overall, the proposed method provides an effective solution for protecting the intellectual property rights of multimedia content owners in video distribution and transmission scenarios.

A blind watermarking approach based on hybrid Imperialistic Competitive Algorithm and SURF points for color Images’ authentication

Currently, we are in the area of the digital big data world. Every day, people share billions of images over Internet from various sources. Ensuring images’ authorship rights and illegal modification and/or reproductions become a challenge. Their protection must meet current and secure development needs of multimedia applications. In this paper, we propose an efficient watermarking scheme based on combining Imperialistic Competitive Algorithm (ICA), Speed Up Robust Features (SURF) points and Discrete Cosine Transform (DCT). The high complexity and inappropriate embedding area could lead to weak performance of watermarking schemas. To address these problems, an ideal watermarking embedding area was proposed using ICA to provide an optimized cost and complexity. The watermark was embedded into the DCT coefficients of the selected embedding areas. The watermark was extracted in a blind way to ensure color image’ authentication. Experimental findings demonstrate that the proposed approach achieves promising performance in terms of robustness and imperceptibility in almost cases. The average PSNR exceeds 61db regarding the imperceptibility. For the robustness measured by the correlation coefficients between the original watermark and the extracted is close to 1 in almost cases.

ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

Underwater imagery and transmission possess numerous challenges like lower signal bandwidth, slower data transmission bit rates, Noise, underwater blue/green light haze etc. These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques. Due to the presence of blue/green light in underwater imagery, shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region. This method is proposed to efficiently deploy an Extreme Learning Machine (ELM) model-based shape adaptive Discrete Cosine Transformation (DCT) for underwater images. Underwater color image restoration techniques based on veiling light estimation and restoration of images followed by Saliency map estimation based on Gray Level Co-occurrence Matrix (GLCM) features are explained. An ELM network is modeled which takes two parameters, signal strength and saliency value of the region to be compressed and level of compression (DCT coefficients and compression steps) are predicted by ELM. This method ensures lesser errors in the Region of Interest and a better trade-off between available signal strength and compression level.

Near-lossless Compression of Tc-99 m DMSA Scan Images Using Discrete Cosine Transformation.

The objective of this study was to optimize the threshold for discrete cosine transform (DCT) coefficients for near-lossless compression of Tc-99 m Dimercaptosuccinic acid (DMSA) scan images using discrete cosine transformation. Two nuclear medicine (NM) Physicians after reviewing several Tc-99 m DMSA scan images provided 242 Tc-99 m DMSA scan images that had scar. These Digital imaging and communication in medicine (DICOM) images were converted in the Portable Network Graphics (PNG) format. DCT was applied on these PNG images, which resulted in DCT coefficients corresponding to each pixel of the image. Four different thresholds equal to 5, 10, 15, and 20 were applied and then inverse discrete cosine transformation was applied to get the compressed Tc-99 m DMSA scan images. Compression factor was calculated as the ratio of the number of nonzero elements after thresholding DCT coefficients to the number of nonzero elements before thresholding DCT coefficients. Two NM physicians who had provided the input images visually compared the compressed images with its input image, and categorized the compressed images as either acceptable or unacceptable. The quality of compressed images was also assessed objectively using the following eight image quality metrics: perception-based image quality evaluator, structural similarity index measure (SSIM), multiSSIM, feature similarity indexing method, blur, global contrast factor, contrast per pixel, and brightness. Pairwise Wilcoxon signed-rank sum tests were applied to find the statistically significant difference between the value of image quality metrics of the compressed images obtained at different thresholds and the value of the image quality metrics of its input images at the level of significance = 0.05. At threshold 5, (1) all compressed images (242 out of 242 Tc-99 m DMSA scan images) were acceptable to both the NM Physicians, (2) Compressed image looks identical to its original image and no loss of clinical details was noticed in compressed images, (3) Up to 96.65% compression (average compression: 82.92%) was observed, and (4) Result of objective assessment supported the visual assessment. The quality of compressed images at thresholds 10, 15, and 20 was significantly better than that of input images at P < 0.0001. However, the number of unacceptable compressed images at thresholds 10, 15, and 20 was 6, 38, and 70, respectively. Up to 96.65%, near-losses compression of Tc-99 m DMSA images was found using DCT by thresholding DCT coefficients at a threshold value equal to 5.

A Hybrid Model by Combining Discrete Cosine Transform and Deep Learning for Children Fingerprint Identification

Fingerprint biometric as an identification tool for children recognition was started in the late 19th century by Sir Galton. However, it is still not matured for children as adult fingerprint identification even after the span of two centuries. There is an increasing need for biometric identification of children because more than one million children are missing every year as per the report of International Centre of missing and exploited children. This paper presents a robust method of children identification by combining Discrete Cosine Transform (DCT) features and machine learning classifiers with Deep learning algorithms. The handcrafted features of fingerprint are extracted using DCT coefficient’s mid and high frequency bands. Gaussian Naïve Base (GNB) classifier is best fitted among machine learning classifiers to find the match score between training and testing images. Further, the Transfer learning model is used to extract the deep features and to get the identification score. To make the model robust and accurate score level fusion of both the models is performed. The proposed model is validated on two publicly available fingerprint databases of children named as CMBD and NITG databases and it is compared with state-of-the-art methods. The rank-1 identification accuracy obtained with the proposed method is 99 %, which is remarkable compared to the literature.

Log-Spectral Amplitude and Spectral Polarity Estimation in Short-Time Discrete Cosine Transform Domain

Single-channel speech enhancement based on short-time spectral amplitude (STSA) estimation often uses the unmodified phase spectrum for speech re-synthesis, thereby introducing undesired artifacts to the enhanced speech. Using discrete Cosine transform (DCT) instead of discrete Fourier transform (DFT) reduces the effects of such issues because the consequences of using noisy DCT polarities for speech re-synthesis are less severe than using the noisy DFT phases. Although DFT-based STSA estimators have been adequately studied in the past, such estimators have not sufficiently been developed for the DCT domain. This study aims to demonstrate the superiority of DCT representation in STSA estimation-based speech enhancement. To achieve this, we first derive the DCT-based STSA estimator which minimizes the mean squared error (MSE) of the log-spectral amplitudes (LSA). We then propose a novel DCT polarity estimator to be used in combination with the STSA estimator. The clean speech DCT coefficients are modeled by a Gaussian or a Laplace density and the noise DCT coefficients are modeled by a Gaussian density. To assess the enhanced speech, objective and subjective quality measures are employed. Results show that the new estimators performed better and are widely preferred by listeners over the corresponding DFT-based estimators. Moreover, the proposed STSA estimators can be expressed in the closed-form, whereas the DFT-based estimator with super-Gaussian speech prior has no closed-form solutions.

Frequency domain-based bit-depth enhancement method and dealing with different bit-depth degradation

In this paper, we present the effectiveness of a frequency domain-based loss function using the discrete cosine transform (DCT) for the bit-depth enhancement (BDE) problem that recovers high-bit-depth images from low-bit-depth images. By minimizing the loss between the DCT coefficients, it is expected to recover smooth luminance changes by suppressing extra frequency components caused by the weak gradients contained in the false contour artifacts. Moreover, we proposed a frequency domain-based multi-level BDE method to deal with different bit-depth degradation. The proposed multi-level BDE method identifies the bit-depth of the input image by embedding the bit-depth information in the frequency domain, and it recovered the missing lower bits appropriately. Experimental results show that the model optimized with frequency-based loss outperforms the model optimized with other losses in the comparison considering the objective and subjective results. Furthermore, we show that the proposed multi-level BDE method is effective for more severe bit-depth degradation on a specific benchmark dataset.

Hybrid Watermarking and Encryption Techniques for Securing Medical Images

Securing medical data while transmission on the network is required because it is sensitive and life-dependent data. Many methods are used for protection, such as Steganography, Digital Signature, Cryptography, and Watermarking. This paper introduces a novel robust algorithm that combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) digital image-watermarking algorithms. The host image is decomposed using a two-dimensional DWT (2D-DWT) to approximate low-frequency sub-bands in the embedding process. Then the sub-band low-high (LH) is decomposed using 2D-DWT to four new sub-bands. The resulting sub-band low-high (LH1) is decomposed using 2D-DWT to four new sub-bands. Two frequency bands, high-high (HH2) and high-low (HL2), are transformed by DCT, and then the SVD is applied to the DCT coefficients. The strongest modified singular values (SVs) vary very little for most attacks, which is an important property of SVD watermarking. The two watermark images are encrypted using two layers of encryption, circular and chaotic encryption techniques, to increase security. The first encrypted watermark is embedded in the S component of the DCT components of the HL2 coefficients. The second encrypted watermark is embedded in the S component of the DCT components of the HH2 coefficients. The suggested technique has been tested against various attacks and proven to provide excellent stability and imperceptibility results.

A multi-level approach with visual information for encrypted H.265/HEVC videos

High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable information. However, these encryption algorithms cannot meet the needs of customers who need part of the information but not the full information in the video. In many cases, such as professional paid videos or video meetings, users would like to observe some visible information in the encrypted video of the original video to satisfy their requirements in daily life. Aiming at this demand, this paper proposes a multi-level encryption scheme that is composed of lightweight encryption, medium encryption and heavyweight encryption, where each encryption level can obtain a different amount of visual information. First, we employ AES–CTR to generate a pseudo-random number sequence. Then, the main syntax elements in the H.265/HEVC encoding process are encrypted by a pseudorandom sequence. In the lightweight encryption level, the syntax element of the luma intraprediction model is chosen for encryption. In the medium encryption level, the syntax element of the discrete cosine transform (DCT) coefficient sign is employed for scrambling encryption. In the heavyweight encryption level, syntax elements of both the luma intraprediction model and the DCT coefficient sign are encrypted simultaneously by the pseudorandom sequence. It is found that both encrypting the luma intraprediction model (IPM) and scrambling the syntax element of the DCT coefficient sign can achieve the performance of a distorted video in which there is still residual visual information, while encrypting both of them can implement the intensity of encryption and one cannot gain any visual information. The experimental results meet our expectations appropriately, indicating that there is a different amount of visual information in each encryption level. Meanwhile, users can flexibly choose the encryption level according to their various requirements.

DHAN: Encrypted JPEG image retrieval via DCT histograms-based attention networks

In image retrieval, the images to be retrieved are stored on remote servers. Since the images contain amounts of privacy information and the server cannot be fully trusted, people usually encrypt their images before uploading them to the server, which raises the demand for encrypted image retrieval (EIR). Current EIR techniques extract ruled hand-craft features from cipher images first and then build retrieval models (e.g., support vector machine, SVM) by these features, or use deep learning models (e.g., Convolutional Neural Network, CNN) to learn cipher-image representations in an end-to-end manner. However, SVM is not skilled at learning non-linear embedding in complex image database, and end-to-end EIR leads to low image security or retrieval performance because CNN is sensitive to extreme chaotic cipher images. Not-end-to-end EIR offers excellent encryption performance, and deep learning can further mine non-linear embedding from ruled hand-craft features. To this end, we propose a novel EIR scheme, named discrete cosine transform (DCT) Histograms-based Attention Networks (DHAN), which is based on deep learning to enhance expression ability of cipher-image in a not-end-to-end manner. Specifically, the DCT coefficients of images are encrypted by value replacement and block permutation encryption, and then the effective histogram features of DCT coefficients are extracted from the cipher images since the sets of DCT frequency in encrypted images are similar to that of plain images. After that, to dynamically learn the salient features of cipher images, we propose a new module named ResAttention and design deep attention networks to provide retrieval. Extensive experiments on two datasets demonstrate that DHAN not only provides high image security but also greatly improves retrieval performance than that of existing schemes.

Efficient Scrambling-Substitution Image Security Scheme using Chaotic Arnold-Logistic Maps in the Discrete Cosine Transform

This paper introduces an efficient scrambling-substitution image security scheme using chaotic Arnold and Logistic (Arnold-Logistic) maps in the discrete cosine transform (DCT). The Arnold map is employed as a scrambling stage while the Logistic map is employed as a substitution stage. The hybrid Arnold-Logistic mapping is performed in the DCT. The encipherment phase of the introduced DCT-based Arnold-Logistic security scheme begins by applying the DCT to the plainimage and the resulted DCT coefficient of the plainimage are scrambled for m iterations using the Arnold transformation. Then, the Arnold-based transformed DCT coefficients are substituted for n iterations using the Logistic map and the inverse of DCT (IDCT) is employed to produce the cipherimage. The decipherment phase of the introduced DCTbased Arnold-Logistic security scheme is the inverse of the encryption stage and begins by applying the DCT to the cipherimage. The resulted DCT coefficient of the cipherimage is inversely substituted for n iterations using the inverse Logistic map. Then, the inverse Logistic-based transformed DCT coefficients are inversely scrambled for m iterations using the inverse Arnold map and the IDCT is employed to produce the decrypted image. A series of test experiments are applied to investigate the introduced DCTbased Arnold-Logistic security scheme. The outcome results demonstrated the superiority of the introduced DCT-based Arnold-Logistic security scheme from the security point of view.

Blind color image watermarking incorporating a residual network for watermark denoising and super-resolution reconstruction

Watermarking is a technique for hiding secret information in various types of multimedia data to protect intellectual property rights. The integration of deep learning technology with image watermarking is currently reshaping the application and promotion of relevant techniques developed so far. This paper presents a novel type of blind color image watermarking method that embeds a downsized color image into a host color image. Watermarking implementation involves partitioning the host image into non-overlapping blocks of 8 × 8 pixels, performing discrete cosine transform (DCT) for each block of every channel, and then manipulating the magnitudes of three designated DCT coefficients subject to a minimization constraint. The experimental results confirmed that the proposed image watermarking method outperformed six other methods in terms of zero-normalized cross-correlation (ZNCC). Moreover, watermark imperceptibility, as reflected by the measured peak signal-to-noise ratio and mean structural similarity metrics, remained satisfactory. In addition to this new style of color image watermarking, we employed a deep residual network to reduce noise and increase the resolution of the retrieved watermarks. Overall, the residual network achieved a satisfactory ZNCC level (> 0.88) when the watermark images were super-resolved by a factor of sixteen.

Extracting the most important Discrete Cosine Transform (DCT) Coefficients for Image Compression using Deep Learning

Extracting the most important Discrete Cosine Transform (DCT) Coefficients for Image Compression using Deep Learning

Identification of hydraulic conductivity field of a karst aquifer by using transition probability geostatistics and discrete cosine transform with an ensemble method

AbstractKnowledge of the spatial distribution characteristics of hydraulic parameters is essential for the management and protection of karst groundwater resources. In this study, we propose a workflow integrating the transition probability geostatistics (T‐PROGS) and the discrete cosine transform (DCT) with the ensemble smoother with multiple data assimilation (ES‐MDA) method to map the hydraulic conductivity of karst aquifers that usually follow a multimodal distribution. The priori parameter ensemble is constructed from the T‐PROGS and transformed into an approximately Gaussian distributed coefficient field containing critical spatial structural features about the aquifer using DCT. The ES‐MDA method is employed to update the DCT coefficients by assimilating the measurements. In addition, a postprocessing process based on cumulative distribution function (CDF) mapping is used to address the problem of parameters gradually tending to a Gaussian distribution after updating using the ES‐MDA and the inappropriate selection of initial parameter values. In practice, the limited amount of available data makes it difficult to fully capture the spatial distribution of the parameters in the initial ensemble of a single realization. Therefore, we suggest a new strategy for structuring the initial ensemble by mixing samples from multiple realizations. We then apply the proposed approach to four single realization models and a combined multiple realizations model in a field hydraulic tomography investigation of a real karst aquifer. The computed results show that this method can effectively identify the characteristics of the spatial distribution of hydraulic conductivity in karst aquifers. Compared with an individual realization model, the uncertainty of the hydraulic conductivity estimated by the combined multiple realizations model is significantly reduced. Therefore, including more uncertainty of the aquifer in the initial ensemble is beneficial to improve the accuracy of the parameter estimation.

Method of detection of blur and multiplicative noise as digital image processing based on analysis of discrete cosine transformation coefficients

Method of detection of blur and multiplicative noise as digital image processing based on analysis of discrete cosine transformation coefficients

A robust blind watermarking scheme based on lifting wavelet transform and hessenberg decomposition

In this paper, a novel robust blind grayscale image digital watermarking scheme is introduced based on lifting wavelet transform (LWT) in combination with discrete cosine transform (DCT), Hessenberg decomposition, and entropy analysis for copyright protection of multimedia information. At first, the two levels of LWT are applied to the host grayscale image to improve the imperceptibility of the watermarking scheme and then the high-frequency sub-band of the 2-level of LWT is decomposed by DCT. Next, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Hessenberg decomposition performs on each selected block, whereas the first row, first column element of the upper Hessenberg matrix is utilized to hide the watermark. To evaluate the imperceptibility and robustness of the proposed digital watermarking scheme, the peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NC) are utilized to measure the quality and the ability of the proposed watermarking scheme to robust against signal processing operations and geometric attacks. Experimental and analysis results have demonstrated that the proposed scheme is achieved a very good tradeoff between imperceptibility and robustness. The comparison with other scheme have shown that the proposed digital watermarking schemes have a superior performance in terms of imperceptibility and robustness than other.

High-quality restoration image encryption using DCT frequency-domain compression coding and chaos

With the arrival of the age of big data, the amount and types of data in the process of information transmission have increased significantly, and the full-disk encryption mode used by traditional encryption algorithms has certain limitations of the times. In order to further improve the bandwidth efficiency of digital images in the transmission process and the information effectiveness of digital image transmission, this paper proposes an algorithm of high-quality restoration image encryption using DCT frequency-domain compression coding and chaos. Firstly, the image hash value is used for the generation of an encryption key with plaintext correlation, then lightweight chaos is generated based on the key to obtain a pseudo-random sequence. Secondly, the image is partitioned into subblock, and converted from time domain into frequency domain by employing Discrete Cosine Transform (DCT) on each block, then perform quantization operation based on frequency domain information to obtain DCT coefficient matrix. Thirdly, the direct current (DC) coefficients and alternating current (AC) coefficients are extracted in the DCT coefficient matrix and compressed by different encoding methods to obtain two sets of bitstream containing DC coefficient and AC coefficient information. Fourthly, permute the DC coefficient bit stream by the chaotic sequence, and reconstruct it with the AC coefficient bit stream to obtain the frequency domain ciphertext image. Finally, the chaotic sequence is used to diffuse ciphertext, and the processed hash value is hidden in the ciphertext to obtain the final ciphertext. The theoretical and experimental analysis showed that the key length reaches 341 bits, and the PSNR value of the restored image is close to 60, all of which satisfy the theoretical value. Therefore, the algorithm has the characteristics of high compression rate, high-quality image restoration large key space, strong plaintext sensitivity, strong key sensitivity and so on. Our method proposed in this paper is expected to provide a new idea for confidential and secure communication in the age of big data.

A Dual-Adaptive Approach Based on Discrete Cosine Transform for Removal of ECG Baseline Wander

Removal of baseline wander (BW) is an important preprocessing step before manually or automatically interpreting electrocardiogram (ECG) records. It is a challenging issue to fully remove BW while preserving original clinical information because BW is usually mingled with low-frequency ECG components. A dual-adaptive approach based on discrete cosine transform (DCT) is presented in this study. Firstly, the cardiac fundamental frequency (CFF) of ECGs is accurately calculated through DCT domain analysis. Secondly, DCT coefficients of ECGs, whose frequencies are below CFF, are used to construct an amplitude vector in which the optimal cut-point between BW and ECGs is distinctly reflected. Finally, a new filtering technique based on DCT is exploited to suppress BW with its cutoff frequency adjusted to the optimal cut-point. The proposed method is applied to both real ECG records and simulated ECGs with its results compared to those of three previous methods published in the literature. The experimental results show that substantial improvements in performance can be achieved when adopting this dual-adaptive approach.