Quantization Table Research Articles

An Adversarial Example Method Based on Dropping Information to Improve Attack Capability

At present, deep neural network has been widely used in many research fields. However, with the in-depth research on artificial intelligence research, it is found that artificial intelligence technology based on deep neural networks bringsconvenience coming with potential security risks. For example, an attacker may misguide the image classification model to output a wrong result with high confidence by adding slight perturbations to a clean image via the adversarial example method. Compared with the previous methods of adding additional information to the images to generate adversarial examples, Ranjie Duan et al. proposed the AdvDrop algorithm. In this algorithm, adversarial examples are generated by deleting the existing information of the images, which is realized by adjusting the quantization step. However, in the quantization process, the AdvDrop algorithm does not consider the different effects of different gradient values of the quantization table on the adversarial effect. In this regard, AdvDrop+ is proposed, that is, in each iteration, the quantization tables are updated according to the gradient numerical values scaled with a factor. To find the proper scaling factor, we find the gradient value of the highest frequency in the gradient histogram and compute its logarithm and the final result is the scaling factor. The experiments show that AdvDrop+ has better attack performance than AdvDrop under the setting of target attack with nearly the same image distortion. At the same time, AdvDrop+ retains the characteristic of AdvDrop, which can drop information.

Enhancing Steganography through Optimized Quantization Tables

Enhancing Steganography through Optimized Quantization Tables

Metaheuristic-based energy-aware image compression for mobile app development

AbstractThe widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables (QTs) for specific images. However, user preferences, like an Android developer’s preference for small-size images, are often overlooked, leading to high-quality images with large file sizes. Another limitation is the lack of comprehensive coverage in current QTs, failing to accommodate all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user’s opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, we suggest a novel representation to tackle the lack of comprehensive coverage. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both representation and objective function changes are independent of the search strategies and can be used with any population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images

Omnidirectional images have gained significant popularity and drawn great attention nowadays, which poses challenges to omnidirectional image processing in solving the bottleneck of storage and transmission. Projecting onto a two-dimensional image plane is generally used to compress an omnidirectional image. However, the most commonly used projection format, the equirectangular projection (ERP), results in a significant amount of redundant samples in the polar areas, thus incurring extra bitrate and geometric distortion. We derive the optimal latitude-adaptive bit allocation for each image tile. Subsequently, we propose a greedy algorithm for non-negative integer bit allocation (NNIBA) for non-uniform quantization under an omnidirectional image quality metric WMSE. In our experiment, we design quantization tables based on JPEG and compare our approach with other sampling-related methods. Our method achieves an average bit saving of 7.9% compared with JPEG while outperforming other sampling-related methods. Besides, we compare our non-uniform quantization approach with two proposed bit allocation methods, achieving an average improvement of 0.35 dB and 2.66 dB under WS-PSNR, respectively. The visual quality assessment also confirms the superiority of our method.

Image compression and reconstruction based on GUI and Huffman coding

Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

Adapting the H.264 Standard to the Internet of Vehicles

We suggest two steps of reducing the amount of data transmitted on Internet of Vehicle networks. The first step shifts the image from a full-color resolution to only an 8-color resolution. The reduction of the color numbers is noticeable; however, the 8-color images are enough for the requirements of common vehicles’ applications. The second step suggests modifying the quantization tables employed by H.264 to different tables that will be more suitable to an image with only 8 colors. The first step usually reduces the size of the image by more than 30%, and when continuing and performing the second step, the size of the image decreases by more than 40%. That is to say, the combination of the two steps can provide a significant reduction in the amount of data required to be transferred on vehicular networks.

CROSS-FREQUENCY DOMAIN FOR JPEG INVERSIBLE WATERMARKING USING MULTIPLE QUANTIZATION TABLES

Reversible data hiding techniques for JPEG images have big challenges in order to improve the capacity, quality, and flexible embedding method. In this article, we propose a new crossdomain using combination of multiple quantization tables in JPEG algorithm. We survey the efficiency of quantization coefficients on the DCT tables in order to decide the way to embed the watermark information. Compared to the conventional method, our experimental results show that our proposed method has better performance in term of both the increasing capacity and improving the quality of embedded JPEG images after data embedding.

Comprehensive Analysis of Compressible Perceptual Encryption Methods-Compression and Encryption Perspectives.

Perceptual encryption (PE) hides the identifiable information of an image in such a way that its intrinsic characteristics remain intact. This recognizable perceptual quality can be used to enable computation in the encryption domain. A class of PE algorithms based on block-level processing has recently gained popularity for their ability to generate JPEG-compressible cipher images. A tradeoff in these methods, however, is between the security efficiency and compression savings due to the chosen block size. Several methods (such as the processing of each color component independently, image representation, and sub-block-level processing) have been proposed to effectively manage this tradeoff. The current study adapts these assorted practices into a uniform framework to provide a fair comparison of their results. Specifically, their compression quality is investigated under various design parameters, such as the choice of colorspace, image representation, chroma subsampling, quantization tables, and block size. Our analyses have shown that at best the PE methods introduce a decrease of 6% and 3% in the JPEG compression performance with and without chroma subsampling, respectively. Additionally, their encryption quality is quantified in terms of several statistical analyses. The simulation results show that block-based PE methods exhibit several favorable properties for the encryption-then-compression schemes. Nonetheless, to avoid any pitfalls, their principal design should be carefully considered in the context of the applications for which we outlined possible future research directions.

Energy-aware JPEG image compression: A multi-objective approach

Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. In the next step, we focused on proposing a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation table (QT) in JPEG image compression. To this end, we have used two general multi-objective approaches: scalarisation and Pareto-based. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. With our proposed scalarisation method, user’s preferences can be set before starting the optimisation process and the algorithm generates only one solution based on the preference, while our Pareto-based approaches generate a set of solutions so that a user can select one of the preferred solutions after the optimisation process.Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms. In particular, EnMOGA, EnMOPS, and EnNSGA-II can perform competitively, among others. From the results, the baseline algorithm in all cases and in comparison to all algorithms yields the worst results. Among the scalarisation methods, EnMOGA and EnMOPS can achieve the first rank in 6 and 7 out of 13 cases and the second rank in 7 and 5 cases in terms of objective function. Also, EnMOES achieved the fifth or worst rank among the scalarisation algorithms. Regarding the Pareto-based algorithms, the table shows that EnNSGAII outperforms EnNSGAIII in 10 out of 13 cases in terms of hyper-volume measure, while it fails in 3 cases. Furthermore, we statistically verify the proposed algorithm’s effectiveness based on the Wilcoxon-signed rank test. Finally, a sensitivity analysis of the parameters is provided. The source code for reproducing the results is available in: https://github.com/SeyedJalaleddinMousavirad/MultiobjectiveJPEGImageCompression.

A self‐distillation object segmentation method via frequency domain knowledge augmentation

A self‐distillation object segmentation method via frequency domain knowledge augmentation

Low-complexity JPEG quantization table requiring only bit-shift operations

ABSTRACT In this paper, a low-complexity quantization table is proposed for the baseline JPEG encoder. The proposed scheme does not require any multiplications or additions; only bit-shift operations are involved. The computational complexity should be drastically reduced without almost incurring any significant performance loss. The resulting approximate quantization table has extremely low arithmetic cost and is compared to state-of-the-art schemes in terms of computational complexity. Comparisons showed that our proposal exhibits better or similar results. The proposed low-complexity quantization table is also investigated in image compression context. It could outperforms other related schemes in low and high-compression scenarios. Comparisons with respect to average PSNR and SSIM are conducted. Experiment outcomes show up to 4.03 dB improvement in average PSNR over the state-of-the-art competitor. The proposed quantization table has a considerably lower computational cost while maintaining much higher image quality.

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a two-step quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to fine-tune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.

A Reliable JPEG Quantization Table Estimator

JPEG compression is a commonly used method of lossy compression for digital images. The degree of compression can be adjusted by the choice of a quality factor QF. Each software associates this value to a quantization table, which is an 8 x 8 matrix used to quantize the DCT coefficients of an image. We propose a method for recovering the JPEG quantization table relying only on the image information, without any metadata from the file header; thus the proposed method can be applied to an uncompressed image format to detect a previous JPEG compression. A statistical validation is used to decide whether significant quantization traces are found or not, and to provide a quantitative measure of the confidence on the detection.

Digital image self-recovery using CNN networks

This paper proposes a novel CNN-based image tampering detection and self-recovery scheme, this method focus on protecting digital images from forensic manipulation. For this purpose, the watermark algorithm should have two main features first, detecting the tampered zone of the received image by using an authentication watermark; second, recovering the tampered zone by utilizing an image digest watermark. For tamper detection, we propose the CNN-based authentication watermark generation. The image digest is generated using a DCT transform and a jpeg quantization table. “An End-to-End Compression Framework Based on CNN” is used to compress the image digest and improve the quality of recovering the tampered area. We code the compressed image digest by Reed-Solomon error correction code to protect it from a high tampering ratio. Finally, we use Arnold transform for embedding the authentication watermark and image digest watermark in the host image. This embedding method improves security issues. To show the proposed scheme's performance, conduct many experiments. The precision and recall score are calculated to evaluate tampering detection, while for evaluation of image self-recovery, calculate the PSNR and SSIM values of the recovered image. The comparisons with the state-of-the-art methods show that the proposed scheme is superior in imperceptibility, security, and recovery capability.

DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms

In this article, we aim to detect the double compression of MPEG-4, a universal video codec that is built into surveillance systems and shooting devices. Double compression is accompanied by various types of video manipulation, and its traces can be exploited to determine whether a video is a forgery. To this end, we present a neural network-based approach with discriminant features for capturing peculiar artifacts in the discrete cosine transform (DCT) domain caused by double MPEG-4 compression. By analyzing the intra-coding process of MPEG-4, which performs block-DCT-based quantization, we exploit multiple DCT histograms as features to focus on the statistical properties of DCT coefficients on multiresolution blocks. Furthermore, we improve detection performance using a vectorized feature of the quantization table on dense layers as auxiliary information. Compared with neural network-based approaches suitable for exploring subtle manipulations, the experimental results reveal that this work achieves high performance.

Rate–distortion optimal evolutionary algorithm for JPEG quantization with multiple rates

The JPEG standard is used extensively in image-related applications. The optimization on a JPEG-standard compressor is of high importance for image storage and transmission. In this paper, we propose a versatile and efficient quantization optimization method for image compression. It is based on multi-objective optimization and is JPEG standard-compatible. Unlike the existing optimization methods, our method fully considers the rate–distortion optimal principle and provides several optimal solutions to address the multiple-rate requirement in applications. In the multi-objective evolutionary optimization framework, the fitness of each quantization table for JPEG compression is evaluated efficiently by the searching in a look-up table, which is constructed based on the statistics of each DCT band in a pre-defined manner. Then, the population update strategy based on the rate–distortion optimal principle is recommended to guide the evolution toward the best rate–distortion performance. Furthermore, to maintain the population’s diversity and uniformity, convex-hull based environmental selection is recommended to identify the solutions at the first Pareto front, and the identified solutions are enriched further with scaling operation to fill the blank rate range. The experimental results for several classic datasets demonstrate the superiority of our method in terms of solution distribution, coding efficiency, and computational complexity.

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table have been developed. The existing solutions to the lossless compression problem for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account inter-channel correlation and the Walsh-Hadamard transformation, characterized by data transformation with a decrease in the range of the initial values by forming a set of channel groups [10–15] with high intra-group correlation [0.9–1] of the corresponding pairs with the selection of optimal parameters. The results obtained in the course of the research allow us to determine the optimal parameters for compression: the results of the compression ratio indicators were improved by more than 30 % with an increase in the size of the parameter channels. This is due to the fact that the more values to be converted, the fewer bits are required to store them. The best values of the compression ratio [8–12] are achieved by choosing the number of channels in an ordered group with high correlation.

An Overview of Double JPEG Compression Detection and Anti-detection

JPEG (Joint Image Experts Group) is currently the most widely used image format on the Internet. Existing cases show that many tampering operations occur on JPEG images. The basic process of the operation is that the JPEG file is first decompressed, modified in the null field, and then the tampered image is compressed and saved in JPEG format, so that the tampered image may be compressed several times. Therefore, the double compression detection of JPEG images can be an important part for determining whether an image has been tampered with, and the study of double JPEG compression anti-detection can further advance the progress of detection work. In this paper, we mainly review the literature in the field of double JPEG compression detection in recent years with two aspects, namely, the quantization table remains unchanged and the quantization table is inconsistent in the double JPEG compression process, Also, we will introduce some representative methods of double JPEG anti-detection in recent years. Finally, we analyze the problems existing in the field of double JPEG compression and give an outlook on the future development direction.

Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient’s data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt the watermarked medical images by using the AES algorithm based on chaotic technique. Arnold’s cat map is used to shuffle the pixel values, and a chaotic Henon map is utilized to generate an aleatory sequence for the AES algorithm. The shuffled watermarked image is encrypted using the modified AES algorithm. The constant of Weber is used to choose the suitable visibility factor for embedding a watermark with high robustness. To control identification, after application of attacks, we use the serial turbo code for correction of the watermark to recover the data inserted. The average peak signal-to-noise ratio (PSNR) of the medical images obtained is 61,7769 dB. Experimental results demonstrate the robustness of the proposed schema against various types of attacks.

Camera Obscura: Exploiting in-camera processing for image counter forensics

For the last two decades Image Forensics has been providing an arsenal of forensic tools to detect tampered images. At the same time, anti-forensics technologies kept evolving to mislead forensic detectors. Such attacks are generally designed to affect a single forensic trace (e.g. JPEG compression, sensor patter noise, histogram statistics) without considering that the image alteration can negatively affect other traces, thus making harder to mimic multiple statistics of natural images. Nevertheless, performing of several attacks can ruin the image quality. In this paper we introduce Camera Obscura, a method that, given a tampered picture, generates a pristine image with native camera metadata, JPEG structure, Quantization Tables, Preview, Thumbnails, their corresponding Quantization Tables, single compression statistics and any in-camera (even proprietary) processing. The attack is performed by exploiting and extending Magic Lantern, an alternative camera firmware, to perform in-camera image buffer substitution. As opposed to available image anti-forensic techniques, Camera Obscura accurately reproduces multiple image statistics in one attack with a limited effect on global image quality. With these characteristics, it can be applied for many different applications such as injecting reference image statistics into a computer generated image, exchange the supposed source of a given image or hide any signal-based editing operation.