Joint Photographic Experts Group Compression Research Articles

Multi-scale image compression and reconstruction algorithm for structural health monitoring system

Image data is increasingly common in structural health monitoring systems. However, due to the limitation of hardware resources such as network bandwidth, the data collected by sensors cannot be transmitted efficiently. In order to solve the problem of inefficient data transmission and ensure data recoverability, an end-to-end compression and reconstruction algorithm with an attention-enhanced residual module and a quantization mechanism is proposed. In order to meet the needs of SHM systems for image diversity, multi-scale images of local and global structural damage are used for research. The performance of the proposed algorithm is compared with traditional image compression standards and baseline models. Experimental results show that the peak signal-to-noise ratio of the reconstructed image is greater than 30 dB at different bit rates, effectively reconstructing the original image information; for images with complex textures, the residual block attention module can improve the rate-distortion performance of the proposed algorithm; when the bit rate is less than 0.44 bit per pixel, the peak signal-to-noise ratio and structural similarity of the reconstructed image of the proposed algorithm are better than those of the autoencoder baseline model and Joint Photographic Experts Group compression method. Structural damage detection examples verify the usability of compressed-reconstructed images in engineering applications.

An Efficient Approximate Multiplier with Encoded Partial Products and Inexact Counter for Joint Photographic Experts Group Compression

An Efficient Approximate Multiplier with Encoded Partial Products and Inexact Counter for Joint Photographic Experts Group Compression

End-to-end compression-aware computer-generated holography.

Joint photographic experts group (JPEG) compression standard is widely adopted for digital images. However, as JPEG encoding is not designed for holograms, applying it typically leads to severe distortions in holographic projections. In this work, we overcome this problem by taking into account the influence of JPEG compression on hologram generation in an end-to-end fashion. To this end, we introduce a novel approach to merge the process of hologram generation and JPEG compression with one differentiable model, enabling joint optimization via efficient first-order solvers. Our JPEG-aware end-to-end optimized holograms show significant improvements compared to conventional holograms compressed using JPEG standard both in simulation and on experimental display prototype. Specifically, the proposed algorithm shows improvements of 4 dB in peak signal-to-noise ratio (PSNR) and 0.27 in structural similarity (SSIM) metrics, under the same compression rate. When maintained with the same reconstruction quality, our method reduces the size of compressed holograms by about 35% compared to conventional JPEG-compressed holograms. Consistent with simulations, the experimental results further demonstrate that our method is robust to JPEG compression loss. Moreover, our method generates holograms compatible with the JPEG standard, making it friendly to a wide range of commercial software and edge devices.

A robust watermarking algorithm against JPEG compression based on multiscale autoencoder

AbstractThe network structure of digital watermarking algorithm based on deep learning is usually encoder‐noise layer‐decoder. Most of the existing encoders suffer from the problem of insufficient feature extraction, and the introduction of simulated differentiable joint photographic experts group (JPEG) compression in the noise layer cannot ensure the robustness under real JPEG. In this paper, a watermarking algorithm based on multi‐scale auto‐encoder is proposed, which can effectively extract the image feature information by combining with the channel attention mechanism. At the same time, some parameters of decoder and encoder are shared to reduce redundant feature embedding and improve extraction accuracy. This paper also proposes a robust training scheme against JPEG compression, which can guide the model to store the watermark in the low‐frequency region needed for decoding. Experimental results show that the peak signal‐to‐noise ratio (PSNR) of the proposed algorithm is above 48 and the decoding rate is above 99% under JPEG compression with quality factor Q = 50. Moreover, this scheme can effectively promote the combination of noise layer in training. In addition, the proposed algorithm is also robust to other common network noises.

Iban plaited mat motif classification with adaptive smoothing

<span lang="EN-US">Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.</span>

Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the rounding error, the truncation error and the quantization coefficient matrix are used to generate a total of 128-dimensional features via a specially designed CNN. In such a CNN, convolutional layers with fixed kernel of and Dropout layers are adopted to prevent overfitting of the model, and an average pooling layer is used to extract local characteristics. In this approach, the Support Vector Machine (SVM) classifier is applied to distinguish whether a given color image is primarily or secondarily compressed. The approach is also suitable for the case when customized needs are considered. The experimental results show that the proposed approach is more effective than some existing ones when the compression quality factors are low.

Digital Image Anti-Forensic Model Using Exponential Chaotic Biogeography-Based Optimization Algorithm

Abstract The innovation in visual imagery has led to massive growth in technologies, wherein digital cameras are obtainable at affordable prices. Thus, the digital images are easily captured and processed due to the internet connectivity. On the other hand, the development of strong image editing software facilitated the forgers to manipulate the accessible images with different tampering operations. Several techniques are devised for detecting the forgeries. Accordingly, this paper devises an anti-forensic model, namely Exponentially Weighted Moving Average-Chaotic Biography Based Optimization (E-CBBO) for joint photographic experts group (JPEG) compression to mitigate the forgeries occurred on the internet while transmitting data. The proposed E-CBBO is designed by integrating the properties of the exponentially weighted moving average (EWMA) with the chaotic biography-based optimization (CBBO). The suggested JPEG anti-forensic model is used to eliminate JPEG compression artifacts through the use of unique deblocking, smoothing with dither and decalibration operations. In addition, the goal is to balance visual quality and forensic undetectability when compressing the JPEG image. The fitness function is developed using the structural similarity index (SSIM), universal image quality index (UIQI) and histogram deviation parameters. With a maximum accuracy of 93.2%, a minimal MSE of 0.110, a maximum SSIM of 0.932 and a maximum UIQI of 0.890, the suggested E-CBBO beat existing approaches.

Detecting Aligned Double JPEG Compressed Color Image With Same Quantization Matrix Based on the Stability of Image

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings.

Design of a nearly linear-phase IIR filter and JPEG compression ECG signal in real-time system

This paper presents the design of an Electrocardiogram (ECG) signal acquisition system based on 32-bit microprocessor platform. In hardware design, we present a complete design similar to the wearable devices used to monitor heart rates over the long term. The firmware is applied by an adaptive data compression method to increase storage space and reduces data transmission time between hardware devices and personal computer (PC). This adaptive algorithm is a combination of Joint Photographic Experts Group (JPEG) compression. The ECG signal was applied to the nearly-linear infinite impulse response (IIR) filter to eliminate noise from ECG signals. There are advantages of nearly-linear IIR filter over traditional IIR that the signal pass through filter is not distorted. Improvements in the JPEG compression algorithm make them suitable for one dimensional signals with a resolution greater than 8 bits and optimize the operation of the Huffman compression as well as the microcontroller unit (MCU) system with low processing speed. Placing the IIR filter at the end of the system promotes the ability to filter while reducing signal distortion after the decompression process. We tested the system with 8 records from the MIT/BIH cardiac arrhythmic database before and after we applied the compression algorithm to the firmware and software in the system that shows the effectiveness of these algorithms. The test results show that the system reached a maximum compression ratio of 8,8. The system deployed on the 32bit MCU (PIC32MZ2048), ECG analog front-end ADS1293 and operation time of ECG device up to 110 h (4.5 days).

Robust steganography resisting JPEG compression by improving selection of cover element

Through minimizing embedding cost, modern adaptive steganography has gained unprecedented success in terms of its undetectability. However, due to Joint Photographic Experts Group (JPEG) compression, the secret bits hidden in the image transmitted over social network platform fail to be perfectly extracted, that remarkably limits its wide application in the real world. In this paper, by improving selection of cover element, we propose to design an enhanced robust steganographic algorithm to resist against JPEG compression. First, since that before and after JPEG compression the sign of Discrete Cosine Transform (DCT) coefficient is not easy to change, we devise cover element based on the sign of DCT coefficient. Furthermore, the selection of cover element is successfully improved with the help of post-processing operation analysis. Second, the embedding cost for each candidate DCT coefficient is calculated. Finally, dependent of both error correction algorithm and syndrome-trellis codes, a compression-resistant stego image is generated with minimum distortion. Numerical results verify that the robustness of our proposed steganographic algorithm is superior to that of current arts; meanwhile, the effectiveness of the algorithm is also verified over social network platform.

SAE based unified double JPEG compression detection system for Web image forensics

PurposeRecently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression.Design/methodology/approachFirst, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression.FindingsExperimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.Practical implicationsTo verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.Originality/valueThis paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

Securing DICOM images by a new encryption algorithm using Arnold transform and Vigenère cipher

This study presents a novel encryption method for the security of DICOM (Digital Imaging and Communications in Medicine) images used in medical applications. The proposed algorithm splits the original image into blocks of size 16 × 16 pixels, then encrypts it through three steps. Firstly, the keys k 1 , k 2 , k 3 and k 4 are transformed from four vectors of 16 pixels to a matrix of 16 × 16 pixels. Then, the proposed algorithm encrypts the image block-by-block using the Vigenere cipher algorithm. For each block, the proposed algorithm modifies the key using Arnold transform. The proposed encryption algorithm is scalable with colour images and JPEG (Joint Photographic Experts Group) compression. The cryptanalysis of the proposed algorithm demonstrates that it passed the cryptography attacks tests with success. Its running time shows that it is faster than a typical and recent image encryption algorithm.

An efficient implementation of low‐power approximate compressor–based multiplier for cognitive communication systems

SummaryImage compression is significant for resourceful transmission and storage of images. The multimedia data like image, audio, and videos are uploaded in the Internet through telecommunication networks. Actual image size plays when page utilization is higher. So image compression standard joint photographic experts group (JPEG) is used for storing images in a compressed format. The quality of JPEG is that it yields little loss in quality using high compression ratios. Multiplier and adder play a major hardware resource in JPEG compression. To reduce this, hardware complexity here proposed a compressor‐based multiplier to increase the performance of JPEG standard as well as reducing the logic resources. This architecture is going to be implemented in CYCLONE IV FPGA with 512 × 512 and 640 × 480 resolutions of images.

Transmission of Still Images Using Low-Complexity Analog Joint Source-Channel Coding.

An analog joint source-channel coding (JSCC) system designed for the transmission of still images is proposed and its performance is compared to that of two digital alternatives which differ in the source encoding operation: Joint Photographic Experts Group (JPEG) and JPEG without entropy coding (JPEGw/oEC), respectively, both relying on an optimized channel encoder–modulator tandem. Apart from a visual comparison, the figures of merit considered in the assessment are the structural similarity (SSIM) index and the time required to transmit an image through additive white Gaussian noise (AWGN) and Rayleigh channels. This work shows that the proposed analog system exhibits a performance similar to that of the digital scheme based on JPEG compression with a noticeable better visual degradation to the human eye, a lower computational complexity, and a negligible delay. These results confirm the suitability of analog JSCC for the transmission of still images in scenarios with severe constraints on power consumption, computational capabilities, and for real-time applications. For these reasons the proposed system is a good candidate for surveillance systems, low-constrained devices, Internet of things (IoT) applications, etc.

Detecting Double JPEG Compressed Color Images With the Same Quantization Matrix in Spherical Coordinates

Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.

Highly Efficient Quality Assessment of 3D-Synthesized Views Based on Compression Technology

Image quality assessment (IQA) technology is facing a new challenge due to the large amount of demand for high-quality 3-D-synthesized views stimulated by the rapid development of virtual reality applications. Since 3-D synthesis views commonly rely on the depth-image-based rendering technology to synthesize virtual images (without reference images in reality and containing geometric distortion), only the no-reference (NR) quality assessment method can meet the requirements. However, most of the current IQA methods for 3-D-synthesized views are full-reference methods. So far, only one specialized NR IQA method for 3-D-synthesized views has been proposed, but its computation is too expensive. For this reason, we have previously proposed a method for extracting geometric distortion regions using the Joint Photographic Experts Group (JPEG) image compression technology to evaluate image quality. In this paper, we consider that although heavy JPEG compression can effectively extract the geometric distortion area, it will ignore the image quality degradation caused by other distortions. Therefore, we have improved our previous work to extract geometric distortions and non-geometric distortions by high-level JPEG compression and low-level JPEG compression, respectively. The overall image quality score was obtained by the fusion of the two results. Experiments indicate that the proposed blind quality model is superior to modern full-, reduced-, and no-reference methods. Compared with our previous work, the performance of the new algorithm has been greatly improved. At the same time, compared with the existing dedicated NR IQA method, the performance is similar but the calculation speed has obvious advantages.

Computationally efficient joint imperceptible image watermarking and JPEG compression: a green computing approach

This paper presents a computationally efficient joint imperceptible image watermarking and joint photographic experts group (JPEG) compression scheme. In recent times, the transmission and storage of digital documents/information over the unsecured channel are enormous concerns and nearly all of the digital documents are compressed before they are stored or transmitted to save the bandwidth requirements. There are many similar computational operations performed during watermarking and compression which lead to computational redundancy and time delay. This demands development of joint watermarking and compression scheme for various multimedia contents. In this paper, we propose a technique for image watermarking during JPEG compression to address the optimal trade-off between major performance parameters including embedding and compression rates, robustness and embedding alterations against different known signal processing attacks. The performance of the proposed technique is extensively evaluated in the form of peak signal to noise ratio (PSNR), correlation, compression ratio and execution time for different discrete cosine transform (DCT) blocks and watermark sizes. Embedding is done on DCT coefficients using additive watermarking.

Joint Photographic Experts Group Compression— A Modified Approach by Low Power Multiplier

Joint Photographic Experts Group Compression— A Modified Approach by Low Power Multiplier

Application of image editing software for forensic detection of image

The image editing software’s available today is apt for creating visually compelling and sophisticated fake images, which causes major issues to the reliability of digital contents as a right representation of reality. Authenticity is the main problem in most digital image communication. Various forensic techniques have been developed for the verification of image integrity, authentication, and tampering detection. Digital image forensics aims at finding the authenticity of images by recovering history of the image. Image manipulations occur at the time of compression, which means changing the DCT coefficients. Forensic technique is capable of detecting chains of operators that is applied to an image. Here the union of Joint Photographic Experts Group compression and full-frame linear filtering were studied and derived an accurate mathematical framework for fully characterizing the probabilistic distributions of the discrete cosine transform (DCT) coefficients of the image, which gets quantized and filtered. This statistical model is used for deriving a set of significant features from the DCT histogram of the input image; these features were fed to a effective classifier which effectively classifies different combinations of linear filtering and compression. Keywords: Full- frame linear filtering, JPEG compression, Linear classifier, image forensics

Denoising using Non Local Linear Filtering and Quantization Matrix Estimation Using ANFIS Algorithm for JPEG Error Analysis to Digital Image Forensics

One of the most commonly used image format is Joint Photographic Experts Group (JPEG). The recognition of JPEG compression plays a significant part in digital forensics. In previous work, JPEG image can be compressed upto n times. However, in the compression techniques noise of the JPEG images and the error analysis in the JPEG images are not primarily concentrated. Hence, the recognition of the JPEG compression results will turn out to be complicated. With the intention of overcoming these concern and eliminate the noise from the image samples, in this study formulated a blend of non local-means filter and its method noise thresholding by means of wavelets. In order to diminish the size of the JPEG image, a Growcut based seam carving technique is employed in this study. Subsequently noises are added to image to carry out Non local Linear Filter (NLF) and its Method Noise Thresholding by means of wavelets (NLFMT) denoising framework. For the purpose of assessing the influence of image compression on the performance of JPEG, a sample Discrete Cosine Transform-Singular Value Decomposition (DCT-SVD) was computed for single and double image compression, images were quantized by means of numerous quantization matrices, quantization matrix results are assessed with the help of Adaptive Neuro Fuzzy Inference System (ANFIS). Based on ANFIS, the elevated frequency coefficients in quantization matrix are employed to make a distinction among singly and doubly compressed images. Extensive experiments and evaluations with previous techniques reveal that the proposed DCT-SVD-ANFIS scheme can discover the double JPEG compression efficiently and noise in the image samples are eliminated with the help of NLFMT methods; it outperforms the existing approaches considerably based on the parameters like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The quantization matrix results were assessed using ANFIS; it has extremely much significance in the field of digital forensics.