39 - DV compression

Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different quantization matrixes. However, when the primary compression and the secondary compression have the same quantization matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same quantization matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a "proper" randomly perturbed ratio. For different images, this universal "proper" ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works.

Image compression techniques are used to reduce the storage and transmission costs. Joint Photographic Experts Group (JPEG) is one of the most popular compression standards in the field of still image compression. In JPEG technique, an input image is decomposed using the DCT, quantized using quantization matrix and further compressed by using entropy encoding. Therefore, the objective of this paper is to carry out the performance analysis of JPEG on the basis of quantization tables. In this paper, we have employed the nelson algorithm for the generation of quantization table; and an attempt has been made for the identification of the best quantization table, because for digital image processing (DIP), it is necessary to discover a new quantization tables to achieve better image quality than the obtained by the JPEG standard. Research has shown that quantization tables used during JPEG compression can also be used to separate images that have been processed by software from those that have not been processed; and it is also used to remove JPEG artefacts or for JPEG recompression.

We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settings-termed its JPEG compression history (CH)-are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.

Digital images contain large amount of information that need evolving effective techniques for storing and transmitting the ever increasing volumes of data. Image compression addresses the problem by reducing the amount of data required to represent a digital image. Discrete Cosine Transformation (DCT), quantization and entropy encoding are the steps in the compression of the JPEG image format. In this proposed work, the performance analysis of JPEG is carried out. Code is written for the Joint Photographic Experts Group (JPEG), the factors Compression Ratio (CR), Signal to Noise Ratio (SNR), Peak SNR (PSNR) and Mean Square Error (MSE) are studied considering different compression ratio for different images. It is found that performance will not remain same for different images even though compression factor was same.

The compression and decompression of continuous-tone images is important in document management and transmission systems. This paper considers an alternative image representation scheme, based on Gaussian derivatives, to the standard discrete cosine transformation (DCT), within a Joint Photographic Experts Group (JPEG) framework. Depending on the computer arithmetic hardware used, the approach developed might yield a compression/decompression technique twice as fast as the DCT and of (essentially) equal quality.

The joint photographic experts group (JPEG) is the most popular format among various digital image formats used in our daily life. Therefore, watermarking JPEG images is important and useful for many applications such as archive management and image authentication. Along with the embedding capacity and imperceptibility that have to be considered for uncompressed images, the storage size of the watermarked JPEG image should be considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we propose a new JPEG image watermarking in the data compression domain, in which only coefficients with values 1 and -1 are expanded to carry message bits, coefficients with values 2 and -2 are shifted, and all the others coefficients remain unchanged. Moreover, a block selection strategy based on the number of 1 and -1 coefficients in each $8 \times 8$ block is proposed, which can be utilized to adaptively choose DCT coefficients for data hiding. Experimental results demonstrate that by using the proposed method, we can easily realize high embedding capacity and good visual quality. Furthermore, the storage size increase for the watermarked JPEG image is well lessened.

Several image compression standards (JPEG, MPEG, H.261) are based on the Discrete Cosine Transform (DCT). These standards do not specify the actual DCT quantization matrix. Ahumada & Peterson and Peterson, Ahumada & Watson provide mathematical formulae to compute a perceptually lossless quantization matrix. Here I show how to compute a matrix that is optimized for a particular image. The method treats each DCT coefficient as an approximation to the local response of a visual `channel.' For a given quantization matrix, the DCT quantization errors are adjusted by contrast sensitivity, light adaptation, and contrast masking, and are pooled non-linearly over the blocks of the image. This yields an 8 X 8 `perceptual error matrix.' A second non-linear pooling over the perceptual error matrix yields total perceptual error. With this model we may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.

One of the hottest areas of development in the computer industry today is motion video on the desktop. Both motion video and still image video pose some new problems: huge storage and high bandwidths are required to send, view or store video data in a timely fashion. Compression techniques may help to reduce the amount of data by trading processing power for reduced data size. For both video compression and decompression, very large amounts of processing power are required to handle the video data quickly enough. Parallel processing is being used in some cases to solve this need for processing power. This paper explores some of the parallel processing techniques currently being used, and some additional possibilities for parallelization. It focuses primarily on JPEG (Joint Photographic Experts Group) techniques, but many of the concepts are applicable to other compression schemes. Results from a simple PC-based JPEG compression/decompression program are used to explore some of the techniques. >

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

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

An image contains large amount of digital data and it is necessary to reduce digital data volume for transmission and preservation by using image compression. This paper mainly concentrates on image compression using Raspberry Pi processor which helps to preserve large number of images and in retaining its quality. Raspberry Pi processor allows the implementation of most widely used 2-D Discrete Cosine Transform (DCT) compression method to give the Joint Photographic Experts Group (JPEG) format using Open CV platform. JPEG compression algorithm is used to compress the full-color still images which give good human visual perception (HVP) after the compression. Experimental results give the high quality compressed images and it is compared with different quality factors. Implementation of image compression on Raspberry Pi has its real time application of Micro Air Vehicles (MAVs). The proposed method helps MAVs to store large number of images captured by it.

The manipulation history of Joint Photographic Experts Group (JPEG) compression plays an important role in JPEG image forensics and information hiding. For non-aligned recompressed images, different cropping methods produce non-aligned outputs with varying feature distributions. One such important factor is the shifts of the discrete cosine transform (DCT) grid (i.e., the misalignment parameters) between two compression processes. Although many methods have been proposed to estimate the misalignment parameters, the limited amount of useful information available in small-sized images leads to low accuracy of these methods. To enhance the accuracy of misalignment parameter estimation for small-sized non-aligned images, we propose a novel two-branch network structure that accounts for the unique horizontal and vertical characteristics of non-aligned images. This structure employs convolution to simulate second-order difference (SOD) and incorporates it throughout the training process to optimize the difference parameters dynamically. Based on the insight that cropping operations leave traces in all color channels, we derive the Cg channel through a color space transformation. This approach expands the input dimensionality to four channels (Y, Cb, Cr, and Cg), thereby compensating for the information scarcity in small-sized images. The experimental results show that our method outperforms existing methods on different image sizes, regardless of the known or unknown quality factor (QF) of the first compression. Finally, we propose a re-cropping framework based on the estimated misalignment parameters. The influence of the first cropping is counteracted by a re-cropping operation, which improves the accuracy of existing methods in estimating the first quantization step for non-aligned recompressed images.

Data hiding (DH) is an important technology for securely transmitting secret data in networks, and has increasing become a research hotspot throughout the world. However, for Joint photographic experts group (JPEG) images, it is difficult to balance the contradiction among embedded capacity, visual quality and the file size increment in existing data hiding schemes. Thus, to deal with this problem, a high-imperceptibility data hiding for JPEG images is proposed based on direction modification. First, this proposed scheme sorts all of the quantized discrete cosine transform (DCT) block in ascending order according to the number of non-consecutive-zero alternating current (AC) coefficients. Then it selects non-consecutive-zero AC coefficients with absolute values less than or equal to 1 at the same frequency position in two adjacent blocks for pairing. Finally, the 2-bit secret data can be embedded into a coefficient-pair by using the filled reference matrix and the designed direction modification rules. The experiment was conducted on 5 standard test images and 1000 images of BOSSbase dataset, respectively. The experimental results showed that the visual quality of the proposed scheme was improved by 1~4 dB compared with the comparison schemes, and the file size increment was reduced at most to 15% of the comparison schemes.

Regarding HDTV postproduction demand for a digital video cassette recorder (DVCR) which is capable of handling a data rate of approx. 1 . 2Gbit/s is substantial. According to the EUREKA EU95 1250/50 interlaced standard an experimental cassette recorder with a 19mm D-2-type cassette has been developed. First results of its implementation show the feasibility of this approach. 1.