Discrete Cosine Transform Coefficients Research Articles

Adaptive Geometric Filtering Based on Average Brightness of the Image and Discrete Cosine Transform Coefficient Adjustment for Gray and Color Image Enhancement

An adaptive geometric filter approach based on average brightness of the image and discrete cosine transform (DCT) coefficient adjustment is proposed in this paper for gray and color image enhancement. Adaptive geometric filter is employed for smoothing the histogram peaks before histogram equalization and DCT coefficient adjustment is applied for enhancing the fine details of the image. The proposed method is compared with the state-of-the-art other contrast enhancement techniques. Experimental results show that proposed technique exceeds the other compared methods both visually and quantitatively. Three metrics are evaluated for numerical assessment that are entropy, quality index and measure of enhancement (EME). Also color quality enhancement metric is tested for color images. After thorough study, it is concluded that our method intensifies the image contrast very well and also preserves the original characteristics of the input images.

Double JPEG Compression Detection Based on Noise-Free DCT Coefficients Mixture Histogram Model

With the wide use of various image altering tools, digital image manipulation becomes very convenient and easy, which makes the detection of image originality and authenticity significant. Among various image tampering detection tools, double JPEG image compression detector, which is not sensitive to specific image tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT (Discrete Cosine Transform) coefficients mixture histogram model. Specifically, we first extract the block-wise DCT coefficients histogram and eliminate the quantization noise which introduced by rounding and truncation operations. Then, for each DCT frequency, a posterior probability can be obtained by solving the DCT coefficients mixture histogram with a simplified model. Finally, the probabilities from all the DCT frequencies are accumulated to give the posterior probability of a DCT block being authentic or tampered. Extensive experimental results in both quantitative and qualitative terms prove the superiority of our proposed method when compared with the state-of-the-art methods.

DWT-DCT Based copyright Protection In Ycbcr Color Space

Digital image watermarking is powerful technique which provide ownership protection and copyright protection. In this paper, a novel watermarking technique based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is presented. YCbCr color model is used for watermark embedding and extraction because of its close resemblance to human visual system. Single level DWT is applied to Luma Component of YCbCr color cover image and then DCT coefficients are taken for watermark embedding process. DCT is applied block by block of size . Binary watermark is scrambled using Arnold transform with k iterations to achieve robustness. Proposed method has been evaluated by many performance evaluation measures such as Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC) and Computational time. Various watermark attacks are also applied against proposed method, result shows that superiority over other methods.

Efficient Security Base d Video Watermarking Technique u sing Chaotic Encryption with Discrete Cosine Transform

In this paper, a novel chaotic encryption-based blind digital video watermarking technique (DVWT) is presented for grayscale and color images. This method makes use of Discrete Cosine transform (DCT) prior to embed the watermark in the hosting video. The hosting video is split into frames of 8*8 nonoverlapping blocks before employing DCT, and watermark bit is implanted by altering the variations among the nearby DCT coefficients of neighboring blocks. Arnold transform is applied for chaotic encryption to offer extra security to the watermark. A set of three versions of the proposed model has been applied and the results are investigated. The experimental outcome confirms that the DCT-VWT is efficient on different image processing functions such as JPEG compression, sharpening, cropping, and median filtering. The comparative analysis ensures that the presented DCT for digital video watermarking technique (DCT-DVWT) is efficient under subjective as well as objective way.

Secure data transmission framework for confidentiality in IoTs

The large-scale data exchange envisioned to occur in distributed IoTs is prone to raise issues regarding privacy, confidentiality and authentication at the cyberspace level. This work focuses to preserve privacy and confidentiality of data in an insecure environment of multimedia exchange between two IoT hops. In order to thwart an adversary, and ensure data confidentiality, we propose a robust multi-level security approach based on information hiding and chaotic theory. Although, some existing block-based robust data hiding schemes based on transform domain offer good results; but the inefficient block and coefficient selection/modification results in poor performance to various commonly occurring cyber-attacks. The proposed scheme is based on Random Coefficient Selection and Mean Modification Approach (RCSMMA). Unlike conventional approaches, which use single coefficient from a block to embed an information bit, RCSMMA uses multiple Discrete Cosine Transform coefficients selected randomly from two non-neighbourhood blocks to ensure information spread over different areas of the cover image. The proposed approach results in very high robustness of the information transferred between two nodes, for all signal processing and the region-oriented attacks, acting simultaneous or standalone. For attacks like rotation, cropping, histogram equalization and sharpening, a very high robustness is achieved with Normalized Cross Correlation close to unity. For other singular/combined attacks average Normalized Cross Correlation is greater than 0.95. The average Peak Signal to Noise Ratio obtained for various images is greater than 40 dB with the payload of 4Kb. The experimental results demonstrate the superiority of the proposed scheme over other state-of-art techniques.

Video Hashing with DCT and NMF

Abstract Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination.

Embedding a Color Watermark into DC coefficients of DCT from Digital Images

In this paper, a secure watermarking framework based on Discrete Cosine Transform (DCT) for embedding a color watermark into digital images that provides robustness against JPEG compression and some other image processing attacks is presented. In the proposed algorithm, the RGB components of the color image have been separated, and then each component is selected to embed a color watermark. The images R, G and B are divided in 8 8 non-overlapped pixels blocks and each block is transformed to frequency domain using DCT. Next, with DC coefficient of each block a new block of 8x8 DC coefficients are arranged and a second DCT is applied. Later, Quantization Index Modulation (QIM) Dither Modulation (DM) is used as the embedding method to insert the color watermark into the 64 resultant coefficients. Meantime, the color watermark is transformed to YCbCr color space and a codification 4:2:0 is used to reduce the quantity of bits to represent it. The proposed watermarking scheme has demonstrated high resistance against various types of attacks in comparison with other state-of-art algorithms.

A Novel Algorithmic Approach for Efficient Realization of 2-D-DCT Architecture for HEVC

Low power realization of high efficiency video coding (HEVC) standard, which has found widespread application in mobile devices, tablets, and high-definition televisions, calls for an efficient approximation of discrete cosine transform (DCT) technique. In this paper, a novel algorithm is proposed to determine the minimum number of low-frequency DCT coefficients required for transform and quantization block in HEVC. Moreover, it introduces hardware efficient 1-D architectures for 4, 8, 16, and 32-point DCT, that make use of the proposed algorithm and conform to the HEVC standard. When these architectures are extended to the HEVC test model (HM), it leads to a minor reduction of PSNR (<;0.15 dB) and a slight increase in bitrate (<;1.5%). The encoding time of HM encoder is reduced up to 16.02% and produces the rate-distortion performance comparable to the reference algorithm. A new memory efficient addressing scheme for transpose memory is presented for 2D-DCT. The proposed 2D-DCT architecture has a low power dissipation and energy consumption of 8.62 mW and 1.77 pJ respectively and can process at least sixty 8K ($7630\times 4830$ ) frames/s. Such architecture with low power and energy characteristics can be integrated into a real-time HEVC encoder for portable consumer electronic products.

Hardware Efficient Architecture for 2D DCT and IDCT Using Taylor-Series Expansion of Trigonometric Functions

This paper presents a hardware architecture for 8 x 8 2D Discrete Cosine Transform (DCT) and Inverse DCT (IDCT) using Taylor-series expansion of trigonometric functions. The processing of DCT/IDCT is modified to eliminate the need for: 1) partitioning the input image into fixed-size blocks and 2) use of transpose buffer for storing intermediate results, achieving low clock cycle time compared to existing techniques. The values obtained from Taylor-series are approximated by fixed-point numbers that reduce the hardware complexity at the cost of acceptable loss in quality of the output image. Based on the fixed-point approach, 18-bit, 24-bit, and 32-bit DCT and IDCT architectures are proposed. This DCT architecture considers the implementation of all 64 coefficients and hence, it is named as DCT64. An important observation about 2D DCT is that most of the compaction energy is concentrated on the low-frequency DCT coefficients which are sufficient to reconstruct the original image. Taking this point into consideration, three DCT architectures, namely, DCT15, DCT10, and DCT6 with 15, 10, and 6 coefficients are designed which are compared with the DCT64. The percentage improvement in the area and performance utilization is obtained for DCT15, DCT10, and DCT6 against DCT64. A gradual increase in the percentage is observed from DCT15 through DCT6 with a marginal decline in the quality of the output image at each step which can be seen from the measured PSNR and SSIM values. Regarding IDCT, the proposed architecture achieved a low percentage of area utilization and similar performance to that of the DCT. The field-programmable gate array (FPGA) implementation of the proposed DCT architecture operates at a higher frequency, achieving lower values of dynamic power, slice reg, slice LUTs metrics that outperforms the processing capabilities of existing Algebraic Integer (AI) and fixed-point implementations.

The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment

Low frequency loop-loop electromagnetic induction (EMI) is a widely-used geophysical measurement method to rapidly measure in situ the apparent electrical conductivity (ECa) of variably-saturated soils. Here, we couple Bayesian inversion of a quasi-two-dimensional electromagnetic (EM) model with image compression via the discrete cosine transform (DCT) for subsurface electrical conductivity (EC) imaging. The subsurface EC distributions are obtained from multi-configuration EMI data measured with a CMD-Explorer sensor along two transects in the Chicken Creek catchment (Brandenburg, Germany). Dipole-dipole electrical resistivity tomography (ERT) data are used to benchmark the inferred EC fields of both transects. We are especially concerned with the impact of the DCT truncation method on the accuracy and reliability of the inversely-estimated EC images. We contrast the results of two different truncation approaches for model parametrization. The first scenario considers an arbitrary selection of the dominant DCT coefficients and their prior distributions (a commonly-used approach), while the second methodology benefits from geostatistical simulation of the EMI data pseudosection. This study demonstrates that DCT truncation based on geostatistical simulations facilitates a robust selection of the dominant DCT coefficients and their prior ranges, resulting in more accurate subsurface EC imaging from multi-configuration EMI data. Results based on geostatistical prior modeling present an excellent agreement between the EMI- and ERT-derived EC fields of the Chicken Creek catchment.

Image compression and encryption scheme with hyper-chaotic system and mean error control

ABSTRACTBased on a hyper-chaotic system, an image compression and encryption scheme is presented by adopting mean error control to improve the quantization compression performance of the discrete cosine transform (DCT) coefficients. The transform coefficients of DCT forming a structure similar to the sub-band can be classified and encoded. Then the image is scanned with Zigzag operation in different directions to achieve sufficiently scrambled effects. The cyclic shift matrix for image encryption is generated by the hyper-chaotic system. The four initial inputs for generating the cyclic shift matrix serve as the key. Simulation results demonstrate the security and the effectiveness of the proposed image compression and encryption scheme.

Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients

AbstractThe purpose of this work is to develop a computer‐aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini‐Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.

USAD: undetectable steganographic approach in DCT domain

ABSTRACTIn this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the secret bits into their corresponding symbols. This mapping technique has to preserve the same dynamics, properties and distribution of the original DCT coefficients. The novelty of our approach is based on the adaptive selection phase of embedding spots. This selection is established through a blindness condition which is applied over each image of the database. The proposed embedding scheme within the middle DCT coefficients shows lower probability of detection and higher flexibility in extraction. We evaluate the detection of our method using the Ensemble Classifiers and a set of frequency and spatial domain feature extractors such as the Spatial domain Rich Model (SRM) features, Chen et al.'s 486-dimensional both inter- and intra-block Markov-based features and Liu's 216-dimensional adaptive steganography-based features.

An improved anti-forensic technique for JPEG compression

The availability of numerous powerful image processing tools or software makes it easier to manipulate the digital information without the addition of any footprints. JPEG compression based tampering detectors are generally employed in digital image forensics. An efficient anti-forensic technique is required to measure the capability of JPEG forensic detectors. In this article, an efficient JPEG anti-forensic technique is presented to hide the JPEG compression artifacts in spatial as well as Discrete Cosine Transform (DCT) domain. We initially employ shifted block DCT approach on the considered JPEG compressed image to fill the gaps in the comb-like distribution of DCT coefficients. This shifted block DCT approach led to the addition of dithering noise itself without the need of any adaptive dithering model. Therefore, the computational cost to create a JPEG forgery reduces significantly. The result of this shifted block DCT approach is further processed in the second step by TV (Total variation)-based deblocking operation to remove the blocking artifacts left during the JPEG compression in the spatial domain. The experimental results illustrate that the presented approach has better performance in comparison to the existing techniques in terms of image visual quality and forensic undetectability with highly reduced computational cost.

Repetitive Iteration CORDIC based Energy Efficient Image Compression for Portable Devices

The rapid advancements in technology in recent years have led to a massive increase in the exchange of data (images, videos, audio, etc.) between portable devices. This has invoked the necessity for building algorithms which consume low power with no compromise in the performance. In this paper, the above captioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation, and performs equally well when compared to Joint Photographic Experts Group (JPEG) standard. Results have been obtained via Matrix Laboratory (MATLAB) and they show that the proposed technique performs equally well and consumes less power in comparison with the other techniques.

An adaptive steganographic scheme for H.264/AVC video with distortion optimization

With the popularity of streaming media, data hiding in H.264/AVC video streams has attracted more and more attention. However, how to achieve high secure video steganography while preventing intra-frame distortion drift is a challenging task. In this paper, based on Syndrome–Trellis Code(STC), we propose a secure steganographic scheme using Quantized Discrete Cosine Transform (QDCT) coefficients of I-frames. We first present a theoretical analysis of the block distortion, which consists of inter-block distortion caused by data embedding in reference pixels of adjacent blocks and inner-block distortion caused by data embedding in local pixels of current block. Based on block distortion analysis, we propose a novel distortion optimization strategy by avoiding inter-block distortion and decreasing inner-block distortion. The cost function is constructed based on block distortion, bit-rate change and texture complexity of DCT block. Compared with distortion compensation methods, the proposed method not only considers the distortion optimization but also introduces secure steganographic scheme. Experimental results show that the proposed scheme can resist the state-of-the-art steganalytic method while achieving high visual quality.

Reversible video data hiding using zero QDCT coefficient-pairs

H.264/Advanced Video Coding (AVC) is one of the most commonly used video compression standard currently. In this paper, we propose a Reversible Data Hiding (RDH) method based on H.264/AVC videos. In the proposed method, the macroblocks with intra-frame $4\times 4$ prediction modes in intra frames are first selected as embeddable blocks. Then, the last zero Quantized Discrete Cosine Transform (QDCT) coefficients in all $4\times 4$ blocks of the embeddable macroblocks are paired. In the following, a modification mapping rule based on making full use of modification directions are given. Finally, each zero coefficient-pair is changed by combining the given mapping rule with the to-be-embedded information bits. Since most of last QDCT coefficients in all $4\times 4$ blocks are zero and they are located in high frequency area. Therefore, the proposed method can obtain high embedding capacity and low distortion.

Partial Image Encryption Based on Using Discrete Cosine Transform Coefficients and Lightweight Stream Algorithm

Partial Image Encryption Based on Using Discrete Cosine Transform Coefficients and Lightweight Stream Algorithm

USING ARTIFICIAL NEURAL NETWORKS FOR TIME DIFFERENCE OF ARRIVAL TARGET LOCALIZATION BASED ON REDUCED DISCRETE COSINE TRANSFORM

The angular coordinates of the pulse source are determined by comparing the signals received simultaneously on several channels. To solve this issue, the application of neural networks is highly important. In this article, the application of the artificial neural network (ANN) approach to the task of target localization is discussed. The research was performed on the basis of a feature extraction technique executed by a discrete cosine transform, which allowed to obtain a compact representation of the signal energy subjected to digital processing. The author defines the angle-of-arrival estimation scheme based on time difference of arrival estimators and the particular problem of estimating constant delays as informative parameters embedded into received signals that are noisy and damped copies of the reference signal. The adaptive element framework is used for synthesis of the feedforward ANN which is fed with the reduced set of the most sensitive discrete cosine transform (DCT) coefficients, which provide a concise representation of the first-order cyclostationary random process. The investigation on the delay estimation accuracy has been carried out to evaluate the performance of the ANNs with different size of their hidden layer and various numbers of the DCT coefficients at their input. It has been proved that five DCT coefficients are enough for the discrimination of the phase shift in the whole range. In turn, it results in the reliable delay estimation produced by the trained ANN that contains eight neurons in its hidden layer.

Toward adaptive BDCT feature representation based image splicing measurement in smart cities

In smart cities, digital image splicing measurement is very important to ensure the security and safety of city monitoring, environment data fusion, cognitive decisions, etc. However, due to images obtained from various environments of cities usually face malevolence splicing, it is hard to perform the authenticity of a legitimate image from smart cities. In this paper, a novel block Discrete Cosine Transform (BDCT) coefficients feature distribution based statistical approach is proposed to discover image forgeries for image splicing measurement. In the proposed feature, all the BDCT neighbouring modes are categorized into a number of groups following the maximum likelihood (ML) criterion to ensure the modes in the same group having similar distributions. For each group, the transition probability matrix (TPM) or the joint probability matrix (JPM) is extracted from the BDCT coefficient pairs in the image. Moreover, the proposed scheme is constructed by concatenating all the TPM/JPM features for each group. Experimental results demonstrate that our feature outperforms two state-of-the-art approaches when taking both the measurement accuracy and feature dimension into consideration.