A Gaussian derivative based version of JPEG for image compression and decompression

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.

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.

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.

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.

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.

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 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.

The Joint Photographic Experts Group (JPEG) standard is widely used for coding still images. The JPEG standard includes two basic compression methods. A DCT-based method is specified for `lossy' compression, and a predictive method for `lossless' compression. JPEG features a simple lossy technique known as the Baseline method which has been by far the most widely implemented method for large number of applications. It is very sensitive to transmission errors and can be employed successfully only when the transmission channel is nearly error free. Wireless communication channels are characterized by long bursts of data errors, and average bit error rates (BERs) of 10-3 to 10-4. This paper presents an efficient scheme to transmit JPEG coded images over wireless channels. The compressed image is protected against various channel effects of wireless channel using Reed Solomon block code and transmitted over wireless channel.

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. >

Parallel Image Compression with JPEG for Multimedia Applications

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

39 - DV compression

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.

To evaluate the effect of different image compression formats of non-analyzed Heidelberg Retina Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) images on the diagnosis of glaucoma by ophthalmologists. Thirty-three topographic and reflectance images taken with the HRT representing different levels of disease were transformed using nine different compression formats. Three independent ophthalmologists, masked as to contour line and stereometric parameters, classified the original and compressed HRT images as normal, suspected glaucoma, or glaucoma, and Kappa agreement coefficients were calculated. The Tagged Image File Format had the largest file size and the Joint Photographic Experts Group (JPEG) 2000 format had the smallest size. The highest Kappa coefficient value was 1.00 for all ophthalmologists using the Tagged Image File Format. Kappa values for JPEG formats were all in the range of good to excellent agreement. Kappa values were lower for Portable Network Graphic and Graphics Interchange Format compression formats. Image compression with JPEG 2000 at a ratio of 20:1 provided sufficient quality for glaucoma analysis in conjunction with a relatively small image size format, and may prove to be attractive for HRT telemedicine applications. Further clinical studies validating the usefulness of interpreting non-analyzed HRT images are required.