Showing papers on "Discrete cosine transform published in 2015"
TL;DR: A novel feature set for steganalysis of JPEG images engineered as first-order statistics of quantized noise residuals obtained from the decompressed JPEG image using 64 kernels of the discrete cosine transform (DCT) (the so-called undecimated DCT).
Abstract: This paper introduces a novel feature set for steganalysis of JPEG images. The features are engineered as first-order statistics of quantized noise residuals obtained from the decompressed JPEG image using 64 kernels of the discrete cosine transform (DCT) (the so-called undecimated DCT). This approach can be interpreted as a projection model in the JPEG domain, forming thus a counterpart to the projection spatial rich model. The most appealing aspect of this proposed steganalysis feature set is its low computational complexity, lower dimensionality in comparison with other rich models, and a competitive performance with respect to previously proposed JPEG domain steganalysis features.
350 citations
TL;DR: Experimental results show that the proposed multiresolution-GFT scheme outperforms H.264 intra by 6.8 dB on average in peak signal-to-noise ratio at the same bit rate.
Abstract: Piecewise smooth (PWS) images (e.g., depth maps or animation images) contain unique signal characteristics such as sharp object boundaries and slowly varying interior surfaces. Leveraging on recent advances in graph signal processing, in this paper, we propose to compress the PWS images using suitable graph Fourier transforms (GFTs) to minimize the total signal representation cost of each pixel block, considering both the sparsity of the signal’s transform coefficients and the compactness of transform description. Unlike fixed transforms, such as the discrete cosine transform, we can adapt GFT to a particular class of pixel blocks. In particular, we select one among a defined search space of GFTs to minimize total representation cost via our proposed algorithms, leveraging on graph optimization techniques, such as spectral clustering and minimum graph cuts. Furthermore, for practical implementation of GFT, we introduce two techniques to reduce computation complexity. First, at the encoder, we low-pass filter and downsample a high-resolution (HR) pixel block to obtain a low-resolution (LR) one, so that a LR-GFT can be employed. At the decoder, upsampling and interpolation are performed adaptively along HR boundaries coded using arithmetic edge coding, so that sharp object boundaries can be well preserved. Second, instead of computing GFT from a graph in real-time via eigen-decomposition, the most popular LR-GFTs are pre-computed and stored in a table for lookup during encoding and decoding. Using depth maps and computer-graphics images as examples of the PWS images, experimental results show that our proposed multiresolution-GFT scheme outperforms H.264 intra by 6.8 dB on average in peak signal-to-noise ratio at the same bit rate.
225 citations
TL;DR: The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.
Abstract: Uniform embedding was first introduced in 2012 for non-side-informed JPEG steganography, and then extended to the side-informed JPEG steganography in 2014. The idea behind uniform embedding is that, by uniformly spreading the embedding modifications to the quantized discrete cosine transform (DCT) coefficients of all possible magnitudes, the average changes of the first-order and the second-order statistics can be possibly minimized, which leads to less statistical detectability. The purpose of this paper is to refine the uniform embedding by considering the relative changes of statistical model for digital images, aiming to make the embedding modifications to be proportional to the coefficient of variation. Such a new strategy can be regarded as generalized uniform embedding in substantial sense. Compared with the original uniform embedding distortion (UED), the proposed method uses all the DCT coefficients (including the DC, zero, and non-zero AC coefficients) as the cover elements. We call the corresponding distortion function uniform embedding revisited distortion (UERD), which incorporates the complexities of both the DCT block and the DCT mode of each DCT coefficient (i.e., selection channel), and can be directly derived from the DCT domain. The effectiveness of the proposed scheme is verified with the evidence obtained from the exhaustive experiments using a popular steganalyzer with rich models on the BOSSbase database. The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.
214 citations
TL;DR: This paper defines a new tensor–tensor product alternative to the t-product and generalizes the transform-based approach to any invertible linear transform, and introduces the algebraic structures induced by each new multiplication in the family, which is that of C⁎-algebras and modules.
184 citations
TL;DR: The results show that the fusion method improves the quality of the output image visually and outperforms the previous DCT based techniques and the state-of-art methods in terms of the objective evaluation.
Abstract: Multi-focus image fusion in wireless visual sensor networks (WVSN) is a process of fusing two or more images to obtain a new one which contains a more accurate description of the scene than any of the individual source images. In this letter, we propose an efficient algorithm to fuse multi-focus images or videos using discrete cosine transform (DCT) based standards in WVSN. The spatial frequencies of the corresponding blocks from source images are calculated as the contrast criteria, and the blocks with the larger spatial frequencies compose the DCT presentation of the output image. Experiments on plenty of pairs of multi-focus images coded in Joint Photographic Experts Group (JPEG) standard are conducted to evaluate the fusion performance. The results show that our fusion method improves the quality of the output image visually and outperforms the previous DCT based techniques and the state-of-art methods in terms of the objective evaluation. 2014 IEEE.
138 citations
TL;DR: A fast and simple recovery algorithm that performs the proposed thresholding approach in the discrete cosine transform domain is proposed and results show that the proposed measurement matrix has a better performance in terms of reconstruction quality compared with random matrices.
Abstract: Compressed sensing (CS) is a technique that is suitable for compressing and recovering signals having sparse representations in certain bases. CS has been widely used to optimize the measurement process of bandwidth and power constrained systems like wireless body sensor network. The central issues with CS are the construction of measurement matrix and the development of recovery algorithm. In this paper, we propose a simple deterministic measurement matrix that facilitates the hardware implementation. To control the sparsity level of the signals, we apply a thresholding approach in the discrete cosine transform domain. We propose a fast and simple recovery algorithm that performs the proposed thresholding approach. We validate the proposed method by compressing and recovering electrocardiogram and electromyogram signals. We implement the proposed measurement matrix in a MSP-EXP430G2 LaunchPad development board. The simulation and experimental results show that the proposed measurement matrix has a better performance in terms of reconstruction quality compared with random matrices. Depending on the compression ratio, it improves the signal-to-noise ratio of the reconstructed signals from 6 to 20 dB. The obtained results also confirm that the proposed recovery algorithm is, respectively, 23 and 12 times faster than the orthogonal matching pursuit (OMP) and stagewise OMP algorithms.
124 citations
TL;DR: A computational method for predicting PPIs using the information of protein sequences by adopting a novel protein sequence representation by using discrete cosine transform on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC).
Abstract: Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.
109 citations
TL;DR: This work focuses on blind compressed sensing, and proposes a framework to simultaneously reconstruct the underlying image as well as the sparsifying transform from highly undersampled measurements, and proves that the proposed block coordinate descent-type algorithms involve highly efficient optimal updates.
Abstract: Natural signals and images are well known to be approximately sparse in transform domains such as wavelets and discrete cosine transform. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synthesis dictionary to reconstruct images from undersampled measurements. In this work, we focus on blind compressed sensing, where the underlying sparsifying transform is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the sparsifying transform from highly undersampled measurements. The proposed block coordinate descent-type algorithms involve highly efficient optimal updates. Importantly, we prove that although the proposed blind compressed sensing formulations are highly nonconvex, our algorithms are globally convergent (i.e., they converge from any initialization) to the set of critical points of the objectives def...
108 citations
TL;DR: In this article, the authors proposed an 8-point DCT approximation that requires only 14 addition operations and no multiplications, compared to state-of-the-art DCT approximations in terms of both algorithm complexity and peak signal-to-noise ratio.
Abstract: Video processing systems such as HEVC requiring low energy consumption needed for the multimedia market has lead to extensive development in fast algorithms for the efficient approximation of 2-D DCT transforms. The DCT is employed in a multitude of compression standards due to its remarkable energy compaction properties. Multiplier-free approximate DCT transforms have been proposed that offer superior compression performance at very low circuit complexity. Such approximations can be realized in digital VLSI hardware using additions and subtractions only, leading to significant reductions in chip area and power consumption compared to conventional DCTs and integer transforms. In this paper, we introduce a novel 8-point DCT approximation that requires only 14 addition operations and no multiplications. The proposed transform possesses low computational complexity and is compared to state-of-the-art DCT approximations in terms of both algorithm complexity and peak signal-to-noise ratio. The proposed DCT approximation is a candidate for reconfigurable video standards such as HEVC. The proposed transform and several other DCT approximations are mapped to systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45 nm CMOS technology.
107 citations
TL;DR: The proposed method constitutes an empirical approach by using the regularized-histogram equalization (HE) and the discrete cosine transform (DCT) to improve the image quality of remote sensing images with higher contrast and richer details without introducing saturation artifacts.
Abstract: In this letter, an effective enhancement method for remote sensing images is introduced to improve the global contrast and the local details. The proposed method constitutes an empirical approach by using the regularized-histogram equalization (HE) and the discrete cosine transform (DCT) to improve the image quality. First, a new global contrast enhancement method by regularizing the input histogram is introduced. More specifically, this technique uses the sigmoid function and the histogram to generate a distribution function for the input image. The distribution function is then used to produce a new image with improved global contrast by adopting the standard lookup table-based HE technique. Second, the DCT coefficients of the previous contrast improved image are automatically adjusted to further enhance the local details of the image. Compared with conventional methods, the proposed method can generate enhanced remote sensing images with higher contrast and richer details without introducing saturation artifacts.
106 citations
TL;DR: A new property of DCT is found, which can be used to obtain a compact representation of an online signature using a fixed number of coefficients, leading to simple matching procedures and providing an effective alternative to deal with time series of different lengths.
Abstract: In this paper, a novel online signature verification technique based on discrete cosine transform (DCT) and sparse representation is proposed. We find a new property of DCT, which can be used to obtain a compact representation of an online signature using a fixed number of coefficients, leading to simple matching procedures and providing an effective alternative to deal with time series of different lengths. The property is also used to extract energy features. Furthermore, a new attempt to apply sparse representation to online signature verification is made, and a novel task-specific method for building overcomplete dictionaries is proposed, then sparsity features are extracted. Finally, energy features and sparsity features are concatenated to form a feature vector. Experiments are conducted on the Sabanci University’s Signature Database (SUSIG)-Visual and SVC2004 databases, and the results show that our proposed method authenticates persons very reliably with a verification performance which is better than those of state-of-the-art methods on the same databases.
TL;DR: A robust watermarking scheme in the encrypted domain is proposed, which protects the original images from the third party embedders and the hybrid discrete wavelet transform and discrete cosine transform based method improves the robust performance of theencrypted domain water marking scheme.
TL;DR: A discrete cosine transform (DCT)-based no-reference video quality prediction model is proposed that measures artifacts and analyzes the statistics of compressed natural videos and is highly correlated with the subjective assessments.
Abstract: A discrete cosine transform (DCT)-based no-reference video quality prediction model is proposed that measures artifacts and analyzes the statistics of compressed natural videos. The model has two stages: 1) distortion measurement and 2) nonlinear mapping. In the first stage, an unsigned ac band, three frequency bands, and two orientation bands are generated from the DCT coefficients of each decoded frame in a video sequence. Six efficient frame-level features are then extracted to quantify the distortion of natural scenes. In the second stage, each frame-level feature of all frames is transformed to a corresponding video-level feature via a temporal pooling, then a trained multilayer neural network takes all video-level features as inputs and outputs, a score as the predicted quality of the video sequence. The proposed method was tested on videos with various compression types, content, and resolution in four databases. We compared our model with a linear model, a support-vector-regression-based model, a state-of-the-art training-based model, and a four popular full-reference metrics. Detailed experimental results demonstrate that the results of the proposed method are highly correlated with the subjective assessments.
07 Jun 2015
TL;DR: The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time using on-line machine-learnt local spatial features to regulate the solution of the underlying inverse problem.
Abstract: Arguably the most common cause of image degradation is compression. This papers presents a novel approach to restoring JPEG-compressed images. The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images. The restoration is a sparse coding process carried out jointy in the DCT and. pixel domains. The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time using on-line machine-learnt local spatial features to regulate the solution of the underlying inverse problem. Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.
TL;DR: Experimental results demonstrate that the proposed technique successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks, such as JPEG compression, noise adding, low-pass filtering, sharpening, and bit-plane removal.
Abstract: This paper presents a new blind and robust image watermarking scheme based on discrete wavelet transform (DWT) and discrete cosine transform (DCT). Two DCT-transformed sub-vectors are used to embed the bits of the watermark sequence in a differential manner. The original sub-vectors are obtained by the sub-sampling of the approximation coefficients of the DWT transform of the host image. During the extraction stage, the simple difference between the corresponding sub-vectors of the watermarked image, gives directly the embedded watermark sequence. Experimental results demonstrate that the proposed technique successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks, such as JPEG compression, noise adding, low-pass filtering, sharpening, and bit-plane removal. Our scheme exhibits also an acceptable to good performance against some geometrical attacks such as resizing and cropping.
TL;DR: In this article, compressive sensing is used to compress and reconstruct a turbulent-flow particle image velocimetry database over a NACA 4412 airfoil, and a proper orthogonal decomposition/principal component analysis as the sparsifying basis is implemented, which outperformed discrete cosine transform.
Abstract: Compressive sensing is used to compress and reconstruct a turbulent-flow particle image velocimetry database over a NACA 4412 airfoil. The spatial velocity data at a given time are sufficiently sparse in the discrete cosine transform basis, and the feasibility of compressive sensing for velocity data reconstruction is demonstrated. Application of the proper orthogonal decomposition/principal component analysis on the dataset works better than the compressive-sensing-based reconstruction approach with discrete cosine transform as the basis in terms of the reconstruction error, although the performance gap between the two schemes is not significant. Using the proper orthogonal decomposition/principal component analysis as the sparsifying basis, compressive-sensing-based velocity reconstruction is implemented, which outperformed discrete cosine transform. Compressive sensing preprocessing (filtering) with discrete cosine transform as the basis is applied to a reduced number of particle image velocimetry snap...
TL;DR: This work presents an efficient semi-local approximation scheme to large-scale Gaussian processes that allows efficient learning of task-specific image enhancements from example images without reducing quality.
Abstract: Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.
TL;DR: It is shown that proposed algorithm involves lower arithmetic complexity compared with the other existing approximation algorithms, and a fully scalable reconfigurable parallel architecture for the computation of approximate DCT based on the proposed algorithm is presented.
Abstract: Approximation of discrete cosine transform (DCT) is useful for reducing its computational complexity without significant impact on its coding performance. Most of the existing algorithms for approximation of the DCT target only the DCT of small transform lengths, and some of them are non-orthogonal. This paper presents a generalized recursive algorithm to obtain orthogonal approximation of DCT where an approximate DCT of length $N$ could be derived from a pair of DCTs of length $(N/2)$ at the cost of $N$ additions for input preprocessing. We perform recursive sparse matrix decomposition and make use of the symmetries of DCT basis vectors for deriving the proposed approximation algorithm. Proposed algorithm is highly scalable for hardware as well as software implementation of DCT of higher lengths, and it can make use of the existing approximation of 8-point DCT to obtain approximate DCT of any power of two length, $N>8$ . We demonstrate that the proposed approximation of DCT provides comparable or better image and video compression performance than the existing approximation methods. It is shown that proposed algorithm involves lower arithmetic complexity compared with the other existing approximation algorithms. We have presented a fully scalable reconfigurable parallel architecture for the computation of approximate DCT based on the proposed algorithm. One uniquely interesting feature of the proposed design is that it could be configured for the computation of a 32-point DCT or for parallel computation of two 16-point DCTs or four 8-point DCTs with a marginal control overhead. The proposed architecture is found to offer many advantages in terms of hardware complexity, regularity and modularity. Experimental results obtained from FPGA implementation show the advantage of the proposed method.
TL;DR: The backward-propagation neural network technique and just-noticeable difference model are incorporated into a block-wise discrete cosine transform (DCT)-based scheme to achieve effective blind image watermarking and apparently exhibits superior robustness and imperceptibility under the same payload capacity.
TL;DR: The results achieved by the method outperformed the auto-correlation (AC)/discrete cosine transform (DCT) method where the DCT coefficients are derived from the AC of ECG segments and fed into the RBF network for classification.
Abstract: This paper proposes a discrete wavelet feature extraction method for an electrocardiogram (ECG)-based biometric system. In this method, the RR intervals are extracted and decomposed using discrete biorthogonal wavelet in wavelet coefficient structures. These structures are reduced by excluding the non-informative coefficients, and then, they are fed into a radial basis functions (RBF) neural network for classification. Moreover, the ability of using only the QT or QRS intervals instead of the RR intervals is also investigated. Finally, the results achieved by our method outperformed the auto-correlation (AC)/discrete cosine transform (DCT) method where the DCT coefficients are derived from the AC of ECG segments and fed into the RBF network for classification. The conducted experiments were validated using four Physionet databases. Critical issues like stability overtime, the ability to reject impostors, scalability and generalization to other datasets have also been addressed.
TL;DR: This paper presents a novel scheme to implement blind image watermarking based on the feature parameters extracted from a composite domain including the discrete wavelet transform (DWT), singular value decomposition (SVD), and discrete cosinetransform (DCT).
TL;DR: The proposed approach of illumination normalization is expected to nullify the effect of illumination variations as well as to preserve the low-frequency details of a face image in order to achieve a good recognition performance.
Abstract: We develop a new approach of illumination normalization for face recognition under varying lighting conditions. The effect of illumination variations is in decreasing order over low-frequency discrete cosine transform (DCT) coefficients. The proposed approach is expected to nullify the effect of illumination variations as well as to preserve the low-frequency details of a face image in order to achieve a good recognition performance. This has been accomplished by using a fuzzy filter applied over the low-frequency DCT (LFDCT) coefficients. The ‘simple classification technique’ (k-nearest neighbor classification) is used to establish the performance improvement by present approach of illumination normalization under high and unpredictable illumination variations. Our fuzzy filter based illumination normalization approach achieves zero error rate on Yale face database B (named as Yale B database in this work) and CMU PIE database. An excellent performance is achieved on extended Yale B database. The present approach of illumination normalization is also tested on Yale face database which comprises of illumination variations together with expression variations and misalignment. Significant reduction in the error rate is achieved by the present approach on this database as well. These results establish the superiority of the proposed approach of illumination normalization, over the existing ones.
30 Jul 2015
TL;DR: Experimental results show that some instances of the proposed GTTs can closely achieve the rate-distortion performance of KLT with significantly less complexity.
Abstract: The Karhunen-Loeve transform (KLT) is known to be optimal for decorrelating stationary Gaussian processes, and it provides effective transform coding of images. Although the KLT allows efficient representations for such signals, the transform itself is completely data-driven and computationally complex. This paper proposes a new class of transforms called graph template transforms (GTTs) that approximate the KLT by exploiting a priori information known about signals represented by a graph-template. In order to construct a GTT (i) a design matrix leading to a class of transforms is defined, then (ii) a constrained optimization framework is employed to learn graphs based on given graph templates structuring a priori known information. Our experimental results show that some instances of the proposed GTTs can closely achieve the rate-distortion performance of KLT with significantly less complexity.
08 Jul 2015
TL;DR: DCTNet is proposed for face recognition in which Discrete Cosine Transform (DCT) as filter banks in place of PCA and an effective method to regulate the block-wise histogram feature vector of DCTNet for robustness is proposed.
Abstract: PCANet was proposed as a lightweight deep learning network that mainly leverages Principal Component Analysis (PCA) to learn multistage filter banks followed by binarization and block-wise histograming. PCANet was shown worked surprisingly well in various image classification tasks. However, PCANet is data-dependence hence inflexible. In this paper, we proposed a data-independence network, dubbed DCTNet for face recognition in which we adopt Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is motivated by the fact that 2D DCT basis is indeed a good approximation for high ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is free from learning as 2D DCT bases can be computed in advance. Besides that, we also proposed an effective method to regulate the block-wise histogram feature vector of DCTNet for robustness. It is shown to provide surprising performance boost when the probe image is considerably different in appearance from the gallery image. We evaluate the performance of DCTNet extensively on a number of benchmark face databases and being able to achieve on par with or often better accuracy performance than PCANet.
TL;DR: The mode decision problem in intra-frame coding is formulated as a Bayesian decision problem based on the newly proposed transparent composite model (TCM) for discrete cosine transform coefficients, and an outlier-based fast intra-mode decision (OIMD) algorithm is presented.
Abstract: In comparison with H.264/Advanced Video Coding, the newest video coding standard, High Efficiency Video Coding (HEVC), improves video coding rate-distortion (RD) performance, but at the price of significant increase in its encoding complexity, especially, in intra-mode decision due to the adoption of more complex block partitions and more candidate intra-prediction modes (IPMs). To reduce the mode decision complexity in HEVC intra-frame coding, while maintaining its RD performance, in this paper, we first formulate the mode decision problem in intra-frame coding as a Bayesian decision problem based on the newly proposed transparent composite model (TCM) for discrete cosine transform coefficients, and then present an outlier-based fast intra-mode decision (OIMD) algorithm. The proposed OIMD algorithm reduces the complexity using outliers identified by TCM to make a fast coding unit split/nonsplit decision and reduce the number of IPMs to be compared. To further take advantage of the outlier information furnished by TCM, we also refine entropy coding in HEVC by encoding the outlier information first, and then the actual mode decision conditionally given the outlier information. The proposed OIMD algorithm can work with and without the proposed entropy coding refinement. Experiments show that for the all-intra-main test configuration of HEVC: 1) when applied alone, the proposed OIMD algorithm reduces, on average, the encoding time (ET) by 50% with 0.7% Bjontegaard distortion (BD)-rate increase and 2) when applied in conjunction with the proposed entropy coding refinement, it reduces, on average, both the ET by 50% and BD-rate by 0.15%.
TL;DR: This paper develops a simple yet very effective detection algorithm to identify decompressed JPEG images that outperforms the state-of-the-art methods by a large margin especially for high-quality compressed images through extensive experiments on various sources of images.
Abstract: To identify whether an image has been JPEG compressed is an important issue in forensic practice. The state-of-the-art methods fail to identify high-quality compressed images, which are common on the Internet. In this paper, we provide a novel quantization noise-based solution to reveal the traces of JPEG compression. Based on the analysis of noises in multiple-cycle JPEG compression, we define a quantity called forward quantization noise. We analytically derive that a decompressed JPEG image has a lower variance of forward quantization noise than its uncompressed counterpart. With the conclusion, we develop a simple yet very effective detection algorithm to identify decompressed JPEG images. We show that our method outperforms the state-of-the-art methods by a large margin especially for high-quality compressed images through extensive experiments on various sources of images. We also demonstrate that the proposed method is robust to small image size and chroma subsampling. The proposed algorithm can be applied in some practical applications, such as Internet image classification and forgery detection.
TL;DR: This paper proposes an energy-efficient CS-based scheme, which is called “treelet-based clustered compressive data aggregation” (T-CCDA), and adopts treelet transform as a sparsification tool to mine sparsity from signals for CS recovery.
Abstract: Compressive sensing (CS)-based data aggregation has become an increasingly important research topic for large-scale wireless sensor networks since conventional data aggregations are shown to be inefficient and unstable in handling huge data traffic. However, for CS-based techniques, the discrete cosine transform, which is the most widely adopted sparsification basis, cannot sufficiently sparsify real-world signals, which are unordered due to random sensor distribution, thus weakening advantages of CS. In this paper, an energy-efficient CS-based scheme, which is called “treelet-based clustered compressive data aggregation” (T-CCDA), is proposed. Specifically, as a first step, treelet transform is adopted as a sparsification tool to mine sparsity from signals for CS recovery. This approach not only enhances the performance of CS recovery but reveals localized correlation structures among sensor nodes as well. Then, a novel clustered routing algorithm is proposed to further facilitate energy saving by taking advantage of the correlation structures, thus giving our T-CCDA scheme. Simulation results show that the proposed scheme outperforms other reference approaches in terms of communication overhead per reconstruction error for adopted data sets.
10 Dec 2015
TL;DR: Novel graph-based transforms (GBTs) for coding inter-predicted residual block signals for video coding significantly outperform traditional DCT and KLT in terms of rate-distortion performance.
Abstract: In video coding, motion compensation is an essential tool to obtain residual block signals whose transform coefficients are encoded. This paper proposes novel graph-based transforms (GBTs) for coding inter-predicted residual block signals. Our contribution is twofold: (i) We develop edge adaptive GBTs (EA-GBTs) derived from graphs estimated from residual blocks, and (ii) we design template adaptive GBTs (TA-GBTs) by introducing simplified graph templates generating different set of GBTs with low transform signaling overhead. Our experimental results show that proposed methods significantly outperform traditional DCT and KLT in terms of rate-distortion performance.
TL;DR: This paper proposes an enhanced technique for blind detection of image splicing that extracts and combines Markov features in spatial and Discrete Cosine Transform domains to detect the artifacts introduced by the tampering operation.
Abstract: Nowadays, it is extremely simple to manipulate the content of digital images without leaving perceptual clues due to the availability of powerful image editing tools. Image tampering can easily devastate the credibility of images as a medium for personal authentication and a record of events. With the daily upload of millions of pictures to the Internet and the move towards paperless workplaces and e-government services, it becomes essential to develop automatic tampering detection techniques with reliable results. This paper proposes an enhanced technique for blind detection of image splicing. It extracts and combines Markov features in spatial and Discrete Cosine Transform domains to detect the artifacts introduced by the tampering operation. To reduce the computational complexity due to high dimensionality, Principal Component Analysis is used to select the most relevant features. Then, an optimized support vector machine with radial-basis function kernel is built to classify the image as being tampered or authentic. The proposed technique is evaluated on a publicly available image splicing dataset using cross validation. The results showed that the proposed technique outperforms the state-of-the-art splicing detection methods.
TL;DR: The DCT-GIST image representation model is introduced which is useful to summarize the context of the scene, and closely matches other state-of-the-art methods based on bag of Textons collected on spatial hierarchy.