Showing papers on "JPEG published in 2008"

Detection of Double-Compression in JPEG Images for Applications in Steganography

Abstract: This paper presents a method for the detection of double JPEG compression and a maximum-likelihood estimator of the primary quality factor. These methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images. The proposed methods use support vector machine classifiers with feature vectors formed by histograms of low-frequency discrete cosine transformation coefficients. The performance of the algorithms is compared to selected prior art.

JPEG image steganalysis utilizing both intrablock and interblock correlations

Abstract: JPEG image steganalysis has attracted increasing attention recently. In this paper, we present an effective Markov process (MP) based JPEG steganalysis scheme, which utilizes both the intrablock and interblock correlations among JPEG coefficients. We compute transition probability matrix for each difference JPEG 2-D array to utilize the intrablock correlation, and "averaged" transition probability matrices for those difference mode 2-D arrays to utilize the interblock correlation. All the elements of these matrices are used as features for steganalysis. Experimental works over an image database of 7,560 JPEG images have demonstrated that this new approach has greatly improved JPEG steganalysis capability and outperforms the prior arts.

Method and Apparatus for Block-Based Compression of Light-field Images

Abstract: A method and apparatus for the block-based compression of light-field images. Light-field images may be preprocessed by a preprocessing module into a format that is compatible with the blocking scheme of a block-based compression technique, for example JPEG. The compression technique is then used to compress the preprocessed light-field images. The light-field preprocessing module reshapes the angular data in a captured light-field image into shapes compatible with the blocking scheme of the compression technique so that blocking artifacts of block-based compression are not introduced in the final compressed image. Embodiments may produce compressed 2D images for which no specific light-field image viewer is needed to preview the full light-field image. Full light-field information is contained in one compressed 2D image.

Detecting doubly compressed JPEG images by using Mode Based First Digit Features

Abstract: In this paper, we utilize the probabilities of the first digits of quantized DCT (discrete cosine transform) coefficients from individual AC (alternate current) modes to detect doubly compressed JPEG images. Our proposed features, named by mode based first digit features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.

Can Commercial Digital Cameras Be Used as Multispectral Sensors? A Crop Monitoring Test

Abstract: The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces.

Example-based learning for single-image super-resolution and JPEG artifact removal

Abstract: This paper proposes a framework for single-image super-resolution and JPEG artifact removal. The underlying idea is to learn a map from input low-quality images (suitably preprocessed low-resolution or JPEG encoded images) to target high-quality images based on example pairs of input and output images. To retain the complexity of the resulting learning problem at a moderate level, a patch-based approach is taken such that kernel ridge regression (KRR) scans the input image with a small window (patch) and produces a patchvalued output for each output pixel location. These constitute a set of candidate images each of which reflects different local information. An image output is then obtained as a convex combination of candidates for each pixel based on estimated confidences of candidates. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as it has been done in existing example-based super-resolution algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing super-resolution and JPEG artifact removal methods shows the effectiveness of the proposed method. Furthermore, the proposed method is generic in that it has the potential to be applied to many other image enhancement applications.

Image Compression with Anisotropic Diffusion

Abstract: Compression is an important field of digital image processing where well-engineered methods with high performance exist. Partial differential equations (PDEs), however, have not much been explored in this context so far. In our paper we introduce a novel framework for image compression that makes use of the interpolation qualities of edge-enhancing diffusion. Although this anisotropic diffusion equation with a diffusion tensor was originally proposed for image denoising, we show that it outperforms many other PDEs when sparse scattered data must be interpolated. To exploit this property for image compression, we consider an adaptive triangulation method for removing less significant pixels from the image. The remaining points serve as scattered interpolation data for the diffusion process. They can be coded in a compact way that reflects the B-tree structure of the triangulation. We supplement the coding step with a number of amendments such as error threshold adaptation, diffusion-based point selection, and specific quantisation strategies. Our experiments illustrate the usefulness of each of these modifications. They demonstrate that for high compression rates, our PDE-based approach does not only give far better results than the widely-used JPEG standard, but can even come close to the quality of the highly optimised JPEG2000 codec.

Using sensor pattern noise for camera model identification

Abstract: Sensor photo-response non-uniformity (PRMJ) was introduced by Lukas et al. [1] to solve the problem of digital camera sensor identification. The PRNU is the main component of a camera fingerprint that can reliably identify a specific camera. This fingerprint can be estimated from multiple images taken by the camera. In this paper, we demonstrate that the same fingerprint can be used for identification of camera brand and model. This is possible due to the fact that fingerprints estimated from images in the TIFF/JPEG format contain local structure due to various in-camera processing that can be detected by extracting a set of numerical features from the fingerprints and classifying them using pattern classification methods. We estimate and classify fingerprints for more than 4500 digital cameras spanning 8 different brands and 17 models. The average probability of correctly classified camera brand was 90.8%.

Image authentication by detecting traces of demosaicing

Abstract: With increasing technical advances, computer graphics are becoming more photorealistic. Therefore, it is important to develop methods for distinguishing between actual photographs from digital cameras and computer generated images. We describe a novel approach to this problem. Rather than focusing on the statistical differences between the image textures, we recognize that images from digital cameras contain traces of resampling as a result of using a color filter array with demosaicing algorithms. We recognize that estimation of the actual demosaicing parameters is not necessary; rather, detection of the presence of demosaicing is the key. The in-camera processing (rather than the image content) distinguishes the digital camera photographs from computer graphics. Our results show high reliability on a standard test set of JPEG compressed images from consumer digital cameras. Further, we show the application of these ideas for accurately localizing forged regions within digital camera images.

Effect of Severe Image Compression on Iris Recognition Performance

Abstract: We investigate three schemes for severe compression of iris images in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying people by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCode templates computed from them for database storage and search; but it is administratively desired that iris data should be stored, transmitted, and embedded in media in the form of images rather than as templates computed with proprietary algorithms. To reconcile that goal with its implications for bandwidth and storage, we present schemes that combine region-of-interest isolation with JPEG and JPEG2000 compression at severe levels, and we test them using a publicly available database of iris images. We show that it is possible to compress iris images to as little as 2000 bytes with minimal impact on recognition performance. Only some 2% to 3% of the bits in the IrisCode templates are changed by such severe image compression, and we calculate the entropy per code bit introduced by each compression scheme. Error tradeoff curve metrics document very good recognition performance despite this reduction in data size by a net factor of 150, approaching a convergence of image data size and template size.

Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]

Abstract: There is an extensive body of literature on image compression, but the central concept is straightforward: we transform the image into an appropriate basis and then code only the important expansion coefficients. The crux is finding a good transform, a problem that has been studied extensively from both a theoretical [14] and practical [25] standpoint. The most notable product of this research is the wavelet transform [9], [16]; switching from sinusoid-based representations to wavelets marked a watershed in image compression and is the essential difference between the classical JPEG [18] and modern JPEG-2000 [22] standards. Image compression algorithms convert high-resolution images into a relatively small bit streams (while keeping the essential features intact), in effect turning a large digital data set into a substantially smaller one. But is there a way to avoid the large digital data set to begin with? Is there a way we can build the data compression directly into the acquisition? The answer is yes, and is what compressive sampling (CS) is all about.

Lifting-based reversible color transformations for image compression

Abstract: This paper reviews a set of color spaces that allow reversible mapping between red-green-blue and luma-chroma representations in integer arithmetic. The YCoCg transform and its reversible form YCoCg-R can improve coding gain by over 0.5 dB with respect to the popular YCrCb transform, while achieving much lower computational complexity. We also present extensions of the YCoCg transform for four-channel CMYK pixel data. Thanks to their reversibility under integer arithmetic, these transforms are useful for both lossy and lossless compression. Versions of these transforms are used in the HD Photo image coding technology (which is the basis for the upcoming JPEG XR standard) and in recent editions of the H.264/MPEG-4 AVC video coding standard. Keywords: Image coding, color transforms, lossless coding, YCoCg, JPEG, JPEG XR, HD Photo. 1. INTRODUCTION In color image compression, usually the input image has three color values per pixel: red, green, and blue (RGB). Independent compression of each of the R, G, and B color planes is possible (and explicitly allowed in standards such as JPEG 2000

Efficient Image Deblocking Based on Postfiltering in Shifted Windows

Abstract: We propose a simple yet effective deblocking method for JPEG compressed image through postfiltering in shifted windows (PSW) of image blocks. The MSE is compared between the original image block and the image blocks in shifted windows, so as to decide whether these altered blocks are used in the smoothing procedure. Our research indicates that there exists strong correlation between the optimal mean squared error threshold and the image quality factor Q, which is selected in the encoding end and can be computed from the quantization table embedded in the JPEG file. Also we use the standard deviation of each original block to adjust the threshold locally so as to avoid the over-smoothing of image details. With various image and bit-rate conditions, the processed image exhibits both great visual effect improvement and significant peak signal-to-noise ratio gain with fairly low computational complexity. Extensive experiments and comparison with other deblocking methods are conducted to justify the effectiveness of the proposed PSW method in both objective and subjective measures.

A machine learning based scheme for double JPEG compression detection

Abstract: Double JPEG compression detection is of significance in digital forensics. We propose an effective machine learning based scheme to distinguish between double and single JPEG compressed images. Firstly, difference JPEG 2D arrays, i.e., the difference between the magnitude of JPEG coefficient 2D array of a given JPEG image and its shifted versions along various directions, are used to enhance double JPEG compression artifacts. Markov random process is then applied to modeling difference 2-D arrays so as to utilize the second-order statistics. In addition, a thresholding technique is used to reduce the size of the transition probability matrices, which characterize the Markov random processes. All elements of these matrices are collected as features for double JPEG compression detection. The support vector machine is employed as the classifier. Experiments have demonstrated that our proposed scheme has outperformed the prior arts.

Multiclass Detector of Current Steganographic Methods for JPEG Format

Abstract: The aim of this paper is to construct a practical forensic steganalysis tool for JPEG images that can properly analyze single- and double-compressed stego images and classify them to selected current steganographic methods Although some of the individual modules of the steganalyzer were previously published by the authors, they were never tested as a complete system The fusion of the modules brings its own challenges and problems whose analysis and solution is one of the goals of this paper By determining the stego-algorithm, this tool provides the first step needed for extracting the secret message Given a JPEG image, the detector assigns it to six popular steganographic algorithms The detection is based on feature extraction and supervised training of two banks of multiclassifiers realized using support vector machines For accurate classification of single-compressed images, a separate multiclassifier is trained for each JPEG quality factor from a certain range Another bank of multiclassifiers is trained for double-compressed images for the same range of primary quality factors The image under investigation is first analyzed using a preclassifier that detects selected cases of double compression and estimates the primary quantization table It then sends the image to the appropriate single- or double-compression multiclassifier The error is estimated from more than 26 million images The steganalyzer is also tested on two previously unseen methods to examine its ability to generalize

Efficient hardware implementation of an image compressor for wireless capsule endoscopy applications

Abstract: The paper presents an area- and power-efficient implementation of an image compressor for wireless capsule endoscopy application. The architecture uses a direct mapping to compute the two-dimensional discrete cosine transform which eliminates the need of transpose operation and results in reduced area and low processing time. The algorithm has been modified to comply with the JPEG standard and the corresponding quantization tables have been developed and the architecture is implemented using the CMOS 0.18um technology. The processor costs less than 3.5k cells, runs at a maximum frequency of 150 MHz, and consumes 10 mW of power. The test results of several endoscopic colour images show that higher compression ratio (over 85%) can be achieved with high quality image reconstruction (over 30 dB).

Perception-based lossy haptic compression considerations for velocity-based interactions

Abstract: The ability of technology to transmit multi-media is very dependent on compression techniques. In particular lossy compression has been used in image compression (jpeg) audio compression (mp3) and video compression (mpg) to allow the transmission of audio and video over broadband network connections. Recently the sense of touch or haptics is becoming more important with its addition in computer games or in cruder applications such as vibrations in a cell phone. As haptic technology improves the ability to transmit compressed force sensations becomes more critical. Most lossy audio and visual compression techniques rely on the lack of sensitivity in humans to pick up detailed information in certain scenarios. Similarly limitations in the sensitivity of human touch could be exploited to create haptic models with much less detail and thus requiring smaller bandwidth. The focus of this paper is on the force thresholds of the human haptic system that can be used in a psychophysically motivated lossy haptic (force) compression technique. Most of the research in this field has measured the just noticeable difference (JND) of the human haptic system with a human user in static interaction with a stationary rigid object. In this paper our focus involves cases where the human user or the object are in relative motion. An example of such an application would be the haptic rendering of the user's hand in contact with of a high-viscous material or interacting with a highly deformable object. Thus an approach is presented to measure the force threshold based on the velocity of the user's hand motion. Two experiments are conducted to detect the absolute force threshold (AFT) of the human haptic system using methodologies from the field of psychophysics. The AFTs are detected for three different ranges of velocity of the user's hand motion. This study implies that when a user's hand is in motion fewer haptic details are required to be stored calculated or transmitted. Finally the implications of this study on a more complete future study will be discussed.

A convolutive mixing model for shifted double JPEG compression with application to passive image authentication

Abstract: The artifacts by JPEG recompression have been demonstrated to be useful in passive image authentication. In this paper, we focus on the shifted double JPEG problem, aiming at identifying if a given JPEG image has ever been compressed twice with inconsistent block segmentation. We formulated the shifted double JPEG compression (SD-JPEG) as a noisy convolutive mixing model mostly studied in blind source separation (BSS). In noise free condition, the model can be solved by directly applying the independent component analysis (ICA) method with minor constraint to the contents of natural images. In order to achieve robust identification in noisy condition, the asymmetry of the independent value map (IVM) is exploited to obtain a normalized criteria of the independency. We generate a total of 13 features to fully represent the asymmetric characteristic of the independent value map and then feed to a support vector machine (SVM) classifier. Experiment results on a set of 1000 images, with various parameter settings, demonstrated the effectiveness of our method.

Further study on YASS: steganography based on randomized embedding to resist blind steganalysis

Abstract: We present further extensions of yet another steganographic scheme (YASS), a method based on embedding data in randomized locations so as to resist blind steganalysis. YASS is a JPEG steganographic technique that hides data in the discrete cosing transform (DCT) coefficients of randomly chosen image blocks. Continuing to focus on JPEG image steganography, we present, in this paper, a further study on YASS with the goal of improving the rate of embedding. Following are the two main improvements presented in this paper: (i) a method that randomizes the quantization matrix used on the transform domain coefficients, and (ii) an iterative hiding method that utilizes the fact that the JPEG "attack" that causes errors in the hidden bits is actually known to the encoder. We show that using both these approaches, the embedding rate can be increased while maintaining the same level of undetectability (as the original YASS scheme). Moreover, for the same embedding rate, the proposed steganographic schemes are more undetectable than the popular matrix embedding based F5 scheme, using features proposed by Pevny and Fridrich for blind steganalysis.

Digital Image Ballistics from JPEG Quantization: A Followup Study

Abstract: The lossy JPEG compression scheme employs a quantization table that controlstheamountofcompressionachieved. Becausedifferentcamerastypicallyemploy different tables, a comparison of an image’s quantization scheme to a database of known cameras affords a simpletechniqueforconfirmingordenying an image’s source. This report describes the analysis of quantization tables extracted from 1,000,000 images downloaded from Flickr.com.

Lossless compression of encrypted grey-level and color images

Abstract: The feasibility of lossless compression of encrypted images has been recently demonstrated by relying on the analogy with source coding with side information at the decoder. However previous works only addressed the compression of bilevel images, namely sparse black and white images, with asymmetric probabilities of black and white pixels. In this paper we investigate the possibility of compressing encrypted grey level and color images, by decomposing them into bit-planes. A few approaches to exploit the spatial and cross-plane correlation among pixels are discussed, as well as the possibility of exploiting the correlation between color bands. Some experimental results are shown to evaluate the gap between the proposed solutions and the theoretically achievable performance.

Image Compression Using Discrete Cosine Transform

Abstract: Image compression is the application of data compression on digital images. Image compression can be lossy or lossless. In this paper it is being attempted to implement basic JPEG compression using only basic MATLAB functions. In this paper the lossy compression techniques have been used, where data loss cannot affect the image clarity in this area. Image compression addresses the problem of reducing the amount of data required to represent a digital image. It is also used for reducing the redundancy that is nothing but avoiding the duplicate data. It also reduces the storage area to load an image. For this purpose we are using JPEG. JPEG is a still frame compression standard, which is based on, the Discrete Cosine Transform and it is also adequate for most compression applications. The discrete cosine transform (DCT) is a mathematical function that transforms digital image data from the spatial domain to the frequency domain.

Unified Lossy and Near-Lossless Hyperspectral Image Compression Based on JPEG 2000

Abstract: We propose a compression algorithm for hyperspectral images featuring both lossy and near-lossless compression. The algorithm is based on JPEG 2000 and provides better near-lossless compression performance than 3D-CALIC. We also show that its effect on the results of selected applications is negligible and, in some cases, better than JPEG 2000.

Weighted Stego-Image Steganalysis for JPEG Covers

Abstract: This paper contains two new results for the quantitative detector of LSB replacement steganography based on a weighted stego-image (WS). First, for spatial domain steganalysis, a variant of the WS method is known to be highly accurate only when cover images have never been subject to lossy compression. We propose a new variant of WS which increases the accuracy for JPEG pre-processed covers by one order of magnitude, thus leaving behind the best structural detectors which were known to be more robust on JPEG pre-compressed covers than WS. Second, we explain why WS-like estimators can also detect LSB replacement steganography in the transformed domain, and derive a reduced-form estimator for JSteg steganography which has equal or slightly better performance than the currently best JSteg detectors.

A unified framework for image compression and segmentation by using an incremental neural network

Abstract: This paper presents a novel unified framework for compression and decision making by using artificial neural networks. The proposed framework is applied to medical images like magnetic resonance (MR), computer tomography (CT) head images and ultrasound image. Two artificial neural networks, Kohonen map and incremental self-organizing map (ISOM), are comparatively examined. Compression and decision making processes are simultaneously realized by using artificial neural networks. In the proposed method, the image is first decomposed into blocks of 8x8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are computed for each block. The dimension of the DCT coefficients vectors (codewords) is reduced by low-pass filtering. This way of dimension reduction is known as vector quantization in the compression scheme. Codewords are the feature vectors for the decision making process. It is observed that the proposed method gives higher compression rates with high signal to noise ratio compared to the JPEG standard, and also provides support in decision-making by performing segmentation.