Digital Image Steganography Based on Combination of DCT and DWT

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.

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.

A lot of attentions has been paid by the researchers for lossy image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality, is still a research issue. In this paper, we proposed an image compression using adaptive scanning, in which DWT(discrete wavelet transform) is performed to source image, DCT(discrete cosine transform) is performed to obtained DWT elements. Next, non zero quantized DCT (discrete cosine transform) coefficients are represented in less number of bits. First, the source image is divided into \(16\times 16\) grids and then DWT transformed, later the obtained elements are divided into \(8\times 8\) grids and then DCT is performed, quantized, differential encoded and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore the indexed vector is divided into two indexed vectors namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.

Image coding is considered to be more effective, as it reduces number of bits required to store and/or to transmit image data. Transform based image coders play a significant role as they decorrelate the spatial low level information. It is found utilization in International compression standards such as JPEG, JPEG 2000, MPEG and H264. The choice of transform is an important issue in all these transforms coding schemes. Most of the literature suggests either Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT). In this proposed work, the energy preservation of DCT coefficients is analysed, and to down sample these coefficients, lifting scheme is iteratively applied so as to compensate the artifacts that appear in the reconstructed picture, and to yield the higher compression ratio. This is followed by scalar quantization and entropy coding, as in JPEG. The performance of the proposed iterative lifting scheme, employed on decorrelated DCT coefficients is measured with standard Peak Signal to Noise Ratio (PSNR) and the results are encouraging.

This paper presents new fast full search motion estimation (ME) algorithms using discrete cosine transform (DCT) coefficients as a gradient magnitude. The proposed algorithms use intra DCT coefficients to arrange the matching scan order. The proposed algorithm 1 uses some of DCT coefficients based on the relationship between gradient magnitude and DCT coefficients, and the proposed algorithm 2 uses all of DCT alternating current (AC) coefficients in order to predict the fluctuation of a block. The proposed algorithms significantly reduce the computational complexity of the conventional algorithm while maintaining the visual quality of video.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

In many ultrasonic imaging applications, the signal acquired is embedded in noise, and situations with very small signal-to-noise ratio (SNR) are not uncommon. Thus, before any data analysis can be applied to the signal some level of noise removal is necessary. In this paper, we analyze the denoising performance of the discrete wavelet transform (DWT), discrete cosine transform (DCT), and Walsh-Hadamard transform (WHT) using an adaptive thresholding function (ATF) that, when applied to DWT, DCT and WHT coefficients, improves the signal-to-noise (SNR) of ultrasonic signals embedded in noise. In particular, the ATF technique is successful in denoising low SNR ultrasonic signals. Furthermore, the ATF approach outperforms the classical techniques when the ultrasonic signal has a low SNR (below 5dB). For signals with uniform noise added to DWT coefficients, the ATF technique achieves SNR improvements around 9dB over the classical thresholding techniques; these improvements are above 10dB for Gaussian noise. �0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5 �0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements. The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit. In this investigation, CS scheme with different transforms is proposed. The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis. The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications. The four testing signals are represented in sparse domain using different transforms. In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients. The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected. The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals. Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT. CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed. CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals. CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.

-With developing of computer networks and digital techniques, the electronic image are easily created, edited, reproduced and distributed. Unfortunately, illegal copy and malicious tamper are also facilitated. At present, fragile watermark is researched greatly to authenticate the veracity and integrity of electronic contents. In this paper, a chaotic watermarking scheme for authentication of popular JPEG images is proposed. The quantized DCT ( Discrete Cosine Transform ) coefficients after entropy decoding are mapped to the initial values of the chaotic system, then the generated watermark information by chaotic iteration is embedded into JPEG compressed domain. Thanks to the high sensitivity on initial values of the chaotic mapping, the very accurate localization is realized for the malicious tampers to JPEG images. Because we directly modify the DCT coefficients after quantization for embedding the watermark, the proposed method can prevent the invalidation of tamper detection by JPEG re-quantization. Furthermore, because the proposed scheme avoids a large of computation on full decoding and re-encoding process, the low complexity and a high extracting speed are obtained. Simulation results show the proposed scheme can directly localize the tampers happened on the watermarked JPEG images. Furthermore, the ability of tamper localization is very sensitivity. So the proposed scheme is very fit for the integrity authentication of the electronic image in Internet.

In this paper, a new block matching criterion is proposed for motion estimation in video coding. The new criterion is based on a comparison between the discrete cosine transform (DCT) coefficients of two blocks to be matched. Since the spectrum of DCT statistically concentrates on the neighborhood of the dc component and the number of non-zero coefficients is quite small, only few coefficients need to be considered when matching blocks according to the new criterion. Almost in all the video coding standards, DCT is a necessary step for spatial redundancy reduction. Thus the generated DCT coefficients can be utilized in the new criterion. However, there are still some blocks whose DCT coefficients are not available. Several algorithms of calculating DCT coefficients of these blocks are given. When this new criterion is combined with the logarithm search, promising results are produced.

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.

Integer Discrete Cosine Transform (DCT) reduces hardware complexity by eliminating floating point multiplication. Multiplier less multiple constant multiplication (MCM) is used to further optimize integer multiplication by replacing it with shifters and adders. As N-point DCT takes 25% of hardware complexity in high efficiency video coding (HEVC), further optimizing DCT in terms speed and power remains critical. Lately, an alternate real-valued DCT coefficients were proposed which reduced the amount of scaling used in Integer DCT coefficients. It achieved the low dynamic range, high speed and low power at same degree of approximation for DCT coefficients. In this paper, an effort is made to further approximate real-valued DCT coefficients of N-point DCT so that critical path due to MCM is reduced. Proposed approximate real-valued DCT coefficients has relatively good PSNR with optimized hardware compared to existing real-valued DCT coefficients. FPGA implementation of 1D-DCT architecture for 32-point DCT with approximate real-valued coefficients shows the reduction of 35% and 24% in area-delay product and power respectively.

This paper presents an efficient image cipher based on applying the chaotic Baker Map (BM) in the Discrete Cosine Transform (DCT). The encryption module of the proposed DCT-based BM image cipher employs a DCT on the original plain-image then, the DCT coefficients of the plain-image are shuffled with the BM. Finally, the inverse DCT is applied to the shuffled plain-image DCT coefficients to obtain the final cipher-image. The decryption module of the proposed DCT-based BM image cipher employs a DCT on the input cipher-image then, the DCT coefficients of the cipher-image are inversely shuffled with the BM. Finally, the inverse DCT is applied to the inversely shuffled cipher-image DCT coefficients to obtain the original plain-image. A set of experimental tests are performed to test the validity of the proposed DCT-based BM image cipher and the performed tests demonstrated the superiority of the proposed DCT-based BM image cipher in terms of statistical, differential, sensitivity and noise immunity.

It is already known that a curve estimated via the discrete cosine transform (DCT) always passes through all the measured points without using imaginary numbers in the DCT coefficients, unlike the discrete Fourier transform. Moreover, owing to its character, the DCT can be used instead of the nonlinear least-squares method to express various theoretical curves. Because the DCT is a kind of Fourier transform, there is a possibility that the DCT could be employed to draw electron density distribution maps of crystals. If so, the probability that the DCT could be used to investigate the internal structure of materials by analysing the theoretical curves would increase. This article reports an attempt to draw the electron density distribution maps of the Mg3BN3 low-pressure phase [Mg3BN3(L)] by using the DCT in order to confirm the utility of the DCT for analysing the internal structure of materials. It is found that the DCT can provide mirror-symmetric electron density distribution projection maps and a modified DCT can be used to calculate whole standard electron density distribution projection maps for the surface plane of the unit cell. Moreover, a real crystal structure that has a centre of symmetry can be determined by the DCT by transforming a 1/4 part of the mirror-symmetric electron density distribution projection map.

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP (Multi-Layer Perceptron) and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform (DCT) algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.