Discrete Cosine Transform Coefficients Research Articles

Robust Image Hashing via CP Decomposition and DCT for Copy Detection

Copy detection is a key task of image copyright protection. This article proposes a robust image hashing algorithm by CP decomposition and discrete cosine transform (DCT) for copy detection. The first contribution is the third-order tensor construction with low-frequency coefficients in the DCT domain. Since the low-frequency DCT coefficients contain most of the image energy, they can reflect the basic visual content of the image and are less disturbed by noise. Hence, the third-order tensor construction with the low-frequency DCT coefficients can ensure robustness of our algorithm. Another contribution is the application of the CP decomposition to the third-order tensor for learning a short binary hash. As the factor matrices learned from the CP decomposition can preserve the topology of the original tensor, the binary hash derived from the factor matrices can reach good discrimination. Lots of experiments and comparisons are done to validate effectiveness and advantage of our algorithm. The results demonstrate that our algorithm has superior classification and copy detection performances than several baseline algorithms. In addition, our algorithm is also better than some baseline algorithms with regard to hash length and computational time.

Multivariate Approach for Texture Segmentation using Probabilistic Statistical Model

The analysis of the regions of the image is of the prerogatives in the fields of medical and global systems meant for location identification. This analysis is strongly associated with partitions of regions of interest such as segmentation. For an effective strategy of analyzing the regions of interest, texture of the image plays a major concern. The texture is generally characterized using signal processing methods namely Discrete Cosine Transformation coefficients and their specific insights leading to feature vector selection. Further, to identify regions, a statistical model needs to be identified for the feature matrix vector and thus make use of Gaussian mixture model with extensions. The Expectation Maximization approach is used, and performance is assessed by experimenting with random images from the Brodatz data store domain. Performance measurements for texture segmentation that can be attributed are Global Cons. Error (GC), Prob. Rand. Index (PR) and Variation of data (VA). These are determined alongside the confusion matrix. To assess the improvement, a comparison was made with other existing models and showed better. The algorithms will be exceptionally helpful for clinical analysis and in radio navigation map systems.

Efficient JPEG image steganography using pairwise conditional random field model

Recent research shows that the security performance of JPEG image steganography can be improved by developing an accurate statistical model to characterize the correlations between Discrete Cosine Transform (DCT) coefficients. In this paper, a novel JPEG steganographic scheme called J-CRF is proposed to characterize the correlations between DCT coefficients using a pairwise Conditional Random Field (CRF) model, in which the correlated DCT coefficient pairs are modeled as bivariate Gaussian random variables. Under the framework of pairwise CRF, the proposed scheme formulates the problem of minimizing the statistical detectability within the Gaussian image model as the minimization of the sum of a series of unary and pairwise potentials, both of which are utilized to measure the local detectability of embedding messages in the DCT coefficient pairs. All the potentials associated with statistical detectability are formalized as the KL divergence between the statistical distributions of the cover and the stego. Experimental results demonstrate that the proposed J-CRF can outperform other SOTA JPEG steganographic schemes, e.g., J-UNIWARD, J-MiPOD, and JEC-RL, in resisting the detection of various advanced steganalyzers. In addition, the performance of J-CRF could be further boosted by reformulating it as the weighted sum of unary and pairwise potentials, called J-CRFW.

Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform

Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called “Color-CADx” for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training–testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.

DeepFake detection based on high-frequency enhancement network for highly compressed content

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ignoring low-quality synthetic content that suffers from high compression. To address this issue, we propose a novel High-Frequency Enhancement framework, which leverages a learnable adaptive high-frequency enhancement network to enrich weak high-frequency information in compressed content without uncompressed data supervision. The framework consists of three branches, i.e., the Basic branch with RGB domain, the Local High-Frequency Enhancement branch with Block-wise Discrete Cosine Transform, and the Global High-Frequency Enhancement branch with Multi-level Discrete Wavelet Transform. Among them, the local branch utilizes the Discrete Cosine Transform coefficient and channel attention mechanism to indirectly achieve adaptive frequency-aware multi-spatial attention, while the global branch supplements the high-frequency information by extracting coarse-to-fine multi-scale high-frequency cues and cascade-residual-based multi-level fusion by Discrete Wavelet Transform coefficients. In addition, we design a Two-Stage Cross-Fusion module to effectively integrate all information, thereby greatly enhancing weak high-frequency information in low-quality data. Experimental results on FaceForensics++, Celeb-DF, and OpenForensics datasets show that the proposed method outperforms the existing state-of-the-art methods and can effectively improve the detection performance of DeepFakes, especially on low-quality data. The code is available here.11https://github.com/Gina-YUE/HiFE.

Uncovering the authorship: Linking media content to social user profiles

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews, an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles.

DCT based multi-head attention-BiGRU model for EEG source location

Electroencephalogram source imaging (ESI) pertains to localize brain sources. Due to the one-to-many relationship between electroencephalogram (EEG) signals and brain sources, ESI becomes a complex inverse problem with poor conditioning, leaving a lot of research space. This article proposes an enhanced multi-head attention (MA) and discrete cosine transform (DCT) based Bidirectional Gated Recurrent Unit (BiGRU) (MA-DCT-BiGRU for short) for addressing the EEG inverse problem. Initially, EEG signal characteristics are captured employing multi-head attention with the inclusion of average hidden states. Then, the DCT is used to project brain source signals into the low, medium, and high frequency subspaces composed of spatial frequency basis vectors. The spatial low-frequency component serves as a filter for extended source reconstruction. Subsequently, BiGRU is employed to learn the mapping from the output of the attention layer to the low frequency DCT coefficients of the brain-derived signals. The simulation results undeniably establish the superiority of the MA-DCT-BiGRU configuration in comparison to other state-of-the-art (SOA) methods for source recovery, irrespective of the source modes and signal-to-noise ratio conditions. Experimental results, utilizing both synthetic and actual epilepsy data, clearly demonstrate the effectiveness of the framework presented in this article for epileptogenic area localization.

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer.

JPEG compression adopts the quantization of Discrete Cosine Transform (DCT) coefficients for effective bit-rate reduction, whilst the quantization could lead to a significant loss of important image details. Recovering compressed JPEG images in the frequency domain has recently garnered increasing interest, complementing the multitude of restoration techniques established in the pixel domain. However, existing DCT domain methods typically suffer from limited effectiveness in handling a wide range of compression quality factors or fall short in recovering sparse quantized coefficients and the components across different colorspaces. To address these challenges, we propose a DCT domain spatial-frequential Transformer, namely DCTransformer, for JPEG quantized coefficient recovery. Specifically, a dual-branch architecture is designed to capture both spatial and frequential correlations within the collocated DCT coefficients. Moreover, we incorporate the operation of quantization matrix embedding, which effectively allows our single model to handle a wide range of quality factors, and a luminance-chrominance alignment head that produces a unified feature map to align different-sized luminance and chrominance components. Our proposed DCTransformer outperforms the current state-of-the-art JPEG artifact removal techniques, as demonstrated by our extensive experiments.

In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform

Digital image watermarking is a crucial technique used to protect the integrity and ownership of digital images by embedding imperceptible watermarks into the image content. This review concentrates on the utilization of discrete cosine transform (DCT) and discrete wavelet transform (DWT) in digital image watermarking schemes. DCT, widely used in image compression like JPEG, is an attractive choice for watermarking, modifying DCT coefficients with minimal impact on image quality. On the other hand, DWT offers multiresolution representation, enabling better localization and robustness against attacks. DWT-based methods use wavelet coefficients to embed watermarks in specific frequency bands or image regions. The review examines the strengths and weaknesses of DCT and DWT in digital image watermarking, exploring algorithms and approaches proposed in the literature. It also addresses challenges like attacks, synchronization, and robustness to image processing. Additionally, a comparative analysis of DCT and DWT-based methods considers imperceptibility, robustness, capacity, and computational complexity. By offering valuable insights, this review aids researchers and practitioners in implementing secure and efficient digital image watermarking solutions.

Analysis of DWT-DCT watermarking algorithm on digital medical imaging.

Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images. In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR. The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76dB. The proposed model works well for clinical medical images when compared with the existing techniques.

HIGH DYNAMIC RANGE IMAGE COMPRESSION ON COMMODITY HARDWARE FOR REAL-TIME MAPPING APPLICATIONS

Abstract. This paper describes a lossy compression scheme for high dynamic range graylevel and color imagery for data transmission purposes in real-time mapping scenarios. The five stages of the implemented non-standard transform coder are written in portable C++ code and do not require specialized hardware to run. Storage space occupied by the bitmaps is reduced via a color space change, 2D integer discrete cosine transform (DCT) approximation, coefficient quantization, two-size run-length encoding and dictionary matching hinged on the LZ4 algorithm. Quantization matrices to eliminate insignificant DCT coefficients are derived from a representative image set through genetic optimization. The underlying fitness function incorporates the obtained output size, classic image quality metrics and the unique color count. Together with a zone-based adaptation mechanism, this allows to specify target bitrates instead of percentage values or abstract quality factors for the reduction rate to be directly matched to the available communication channel capacities. Results on a camera control unit of a fixed-wing unmanned aircraft system built around entry-level PC hardware revealed single-thread compression and decompression throughputs of several hundred mebibytes per second for full-swing 16 and 32 bit RGB imagery at medium compression ratios. A degradation in image quality compared to popular compression libraries could be identified, however, at acceptable levels statistically and visually.

Reversible Data Hiding of JPEG Image Based on Adaptive Frequency Band Length

JPEG images are widely used on the Internet. Histogram shifting reversible data hiding (RDH) methods based on quantized DCT (discrete cosine transform) coefficients are a research focus for JPEG images. Among them, frequency band selection is a key step that affects the performance of JPEG image RDH. In the existing algorithm, frequency band selection is to evaluate the embedding performance of the whole frequency band. But after DCT block sorting, the distribution of expand AC (alternating current) coefficients (Coefficients that can carry data) is in the front of the frequency band, so the performance evaluation of the whole frequency band will produce errors. This paper proposed an adaptive frequency band length JPEG image RDH method, which determines the used length of the frequency band while selecting the frequency band, so that can effectively reduce the invalid shift. Instead of selecting the whole frequency band length in a fixed mode for performance evaluation, the optimal combination of frequency band lengths will be selected according to the embedding performance of different lengths of each frequency band. Then, a united solution mechanism is used to solve the optimal frequency band length, and the frequency band selection is also completed while solving the frequency band length. Experimental results show that our algorithm outperforms existing state-of-the-art methods in terms of marked image visual quality and file size increment.

Enhancing Laryngeal Spinocellular Carcinoma Image Security with DCT.

This work proposes a novel secret sharing scheme to enhance the security of Laryngeal Spinocellular Carcinoma or Laryngeal Squamous Cell Carcinoma (LSCC) images using the Discrete Cosine Transformation (DCT) as a cryptographic tool. The DCT-based secret sharing method divides LSCC images into shares, each containing DCT coefficients that represent the image's frequency components. The original image can only be reconstructed when a predefined number of shares are combined, ensuring confidentiality and preventing unauthorized access. The proposed scheme demonstrates robustness against noise and data loss during transmission, preserving image quality and data integrity. The performance of the proposed scheme concerning the quality of the recovered image and the strength of security preservation is demonstrated through PSNR improvement analysis, correlation analysis, and histogram analysis. The efficiency of DCT-based secret sharing enables application in medical settings, facilitating accurate diagnosis and treatment planning for LSCC patients while safeguarding patient privacy.

Efficient source wavefield reconstruction for full-waveform inversion

Full-waveform inversion (FWI) is a useful tool for estimating the properties of subsurface media, but it has the disadvantage of incurring considerable computing costs. Therefore, we develop a method for reducing the computing memory requirement in the time domain of FWI that uses a new approach with the discrete cosine transform (DCT) and the Nyquist sampling theorem. Our method enables accurate reconstruction of a wavefield with several optimal DCT coefficients derived by considering the maximum wavenumber component of the virtual source wavefield and the maximum frequency of the source wavelet. Consequently, virtual source wavefields can be stored in a computer’s memory rather than on a disk. Our FWI algorithm is compared with existing compression methods that use DCT and wavelet transform in a numerical example. The results of this comparison demonstrate that our algorithm provides significantly improved computational efficiency.

New Proposed Mixed Transforms: CAW and FAW and Their Application in Medical Image Classification

The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.

Adaptive bistable stochastic resonance based blind watermark extraction in discrete cosine transform domain

AbstractBlind watermark extraction in discrete cosine transform (DCT) domain has a wide application prospect as well as a challenging subject. The imperceptibility of watermark signal makes watermark extraction a weak signal reception issue in essence. For DCT coefficients of host image generally disobey Gaussian distribution, at which the performance of linear correlated reception is no longer optimal. Aiming at this, a novel blind watermark extraction scheme combining the uncorrelated reception with adaptive bistable stochastic resonance (ABSR) technique is proposed. First, by block DCT transformation for host image, an additive watermark embedding algorithm is introduced, in which the watermarked image can be converted to one dimensional time domain weak signal (binary watermark image) reception under additive Laplacian noise (selected DCT coefficients). On this basis, through the key technology research on quantitatively cooperative resonance relationship under Laplacian noise, the ABSR system can be implemented by bistable system parameters self‐adaptive adjustment, in which the ABSR system output signal will be enhanced rather than be weakened by random noise. Finally, the ABSR‐based watermark extraction scheme is investigated, and both the visual effect, bit error ratio performance and robustness of proposed scheme are testified to be superior to that of traditional uncorrelated extraction.

Time Frequency Analysis-Based Averaging and Fusion of Features for Wearable Non-Invasive Blood Glucose Estimation

This paper proposes a Clark error grid based stacking method for fusing various time frequency averaged features together for reducing the measurement errors for performing the wearable non-invasive blood glucose estimation. Like the conventional time frequency representation based denoising methods, the most critical issue is on the selection of the time frequency components. In this paper, three time frequency representation based averaging methods are investigated. They are the discrete cosine transform (DCT) based averaging method, the singular spectrum analysis (SSA) based averaging method and the empirical mode decomposition (EMD) based averaging method. It is found that retaining 40% of the DCT coefficients as well as three SSA components and three intrinsic mode functions (IMFs) could yield the best blood glucose estimation performances. Two datasets are employed for demonstrating the effectiveness and the robustness of our proposed method. The most common blood glucose estimation method and our proposed method can yield the Pearson’s correlation coefficient (R) at 0.4308 and 0.9172, respectively, the mean absolute error (MAE) at 0.8783 and 0.8193, respectively, the root mean squares error (RMSE) at 1.0680 and 0.9621, respectively, and the mean absolute relative difference (MARD) at 0.1504 and 0.1133, respectively, as well as the percentage of the data falling in the zone A of the Clark error grid at 71.1111% and 90.5882%, respectively, for the data in the first dataset. The results obtained for the data in the second dataset are similar. Obviously, our proposed method significantly outperforms the most common blood glucose estimation methods.

Robust image watermarking in DCT domain using optimal inter‐block difference

SummaryRobust watermarking provides valuable solutions to protect the copyright and secure digital images. In this paper, a robust and transparent watermarking scheme in the discrete cosine transform (DCT) domain is presented. The watermark is embedded into the difference of two adjacent DCT coefficients located at the same frequency. The embedding process involves optimal selection of adjacent blocks and DCT coefficients positions. For a given DCT block, the optimal selection is to find the coefficient with the lowest DCT frequency that requires the minimal variation to represent the target watermark bit. To insert the watermark, a new quantization index modulation (QIM) based method with irregular distance between continuous quantizers is employed. The performance of the proposed scheme was extensively evaluated using 12 benchmark images and a total of more than 90 attacks per image grouped into nine attack types including JPEG compression, filtering and noise contamination. Furthermore, robustness comparison with state‐of‐the‐art works was conducted, and the results demonstrate the superiority of the proposed scheme in terms of imperceptibility and robustness.

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.