Joint Photographic Experts Group Research Articles

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a well-accepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.

Reversible data hiding for JPEG images based on pairwise nonzero AC coefficient expansion

JPEG (Joint Photographic Experts Group) is the most common format in our daily life for storing and transmitting photographic images. Reversible data hiding (RDH) for JPEG images currently is one of the hotspots in the field of data hiding. However, different from RDH in uncompressed images, any modification in the compressed domain may produce distinct visual distortion and significantly increase the file size of the marked image. In this paper, a new JPEG RDH scheme based on pairwise nonzero AC coefficient expansion (pairwise NACE) is proposed. Firstly, by taking every two adjacent nonzero alternating current (AC) coefficients together to generate the nonzero-AC-coefficient pairs (NACPs), a sharp two-dimensional (2D) histogram can be constructed. Then a new reversible 2D mapping is carefully designed, and data are embedded based on the modification of these NACPs according to the mapping. Furthermore, an adaptive embedding strategy based on block and DCT frequency selection is proposed in order to preserve the visual quality and reduce the file size increase of the marked JPEG image. Experimental results demonstrate that our proposed method can achieve better performance both in visual quality and file size preservation compared to the previous works.

Dynamic Deformation Measurement by the Sampling Moir\xe9 Method from Video Recording and its Application to Bridge Engineering

Measuring accurate dynamic deformation distribution for large-scale structures inexpensively and efficiently is a crucial challenge of structural health monitoring. In this study, a simple technique for measuring the deflection and vibration frequency from a captured video, based on the sampling Moiré method is developed. The striking advantage over conventional measurement using sensors and other imaging techniques are that the developed technique allows accurate measurement of two-dimensional deformations at multiple locations as well as the natural frequency of the target structure. Joint Photographic Experts Group (JPEG)-formatted images encoded from the recorded video with Motion-JPEG (MJPEG) format reduced the file size significantly without degrading the measurement accuracy and applied to deformation measurement. The effectiveness of the developed technique was confirmed through a field experiment of an old bridge in Taiwan. The field experiment demonstrated that both deflection and natural frequency of the bridge were successfully measured with higher 0.1 mm displacement accuracy and 0.05 Hz frequecy comparable to a conventional microwave radar displacement sensor from 20 m distance.

Novel and Optimal Luma Modification-Based Chroma Downsampling for Bayer Color Filter Array Images

The Bayer color filter array (CFA) pattern has been widely used in modern digital color cameras, and the captured image is called the Bayer CFA image IBayer. Chroma downsampling is a necessary and important step in the coding system. In this paper, a novel and optimal luma modification-based chroma downsampling (LMCD) method is proposed for IBayer. For each converted 2 x 2 YUV block, the downsampled (U, V)-pair and the modified luma values by the proposed LMCD method can be optimally determined, and the optimal variant of our LMCD method associated with the two modified luma values has the best quality among all sixteen variants of LMCD. Based on the Kodak and IMAX datasets, on the High Efficiency Video Coding (HEVC) platform HM-16.18, the Versatile Video Coding (VVC) platform VTM-8.0, and the Joint Photographic Experts Group (JPEG) platform, the comprehensive experimental data showed the substantial quality and quality-bitrate tradeoff merits of the reconstructed images by our optimal LMCD method relative to the traditional and state-of-the-art chroma downsampling methods.

Dense Light Field Coding: A Survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.

Comparative Evaluation of CephNinja for Android and NemoCeph for Computer for Cephalometric Analysis: A Study to Evaluate the Diagnostic Performance of CephNinja for Cephalometric Analysis.

ABSTRACTBackground:Since the introduction of digitization in cephalometrics, orthodontics has experienced a new horizon. Technological advancement is usually followed by comparisons between the methods.Aims:The aim of this study was to compare values of cephalometric analysis performed by CephNinja and NemoCeph for Downs’s analysis.Settings and Design:This prospective study was conducted using 100 diagnostic digital lateral cephalograms taken from the same machine. The samples were collected by non-probability convenience sampling procedures.Materials and Methods:The diagnostic images were cropped to standard lateral cephalogram film dimension; a scale image was placed on the top for calibration, numbered 1–100 for identification and was saved in Joint Photographic Experts Group (JPEG) format. A laptop with mouse-controlled cursor was used for NemoCeph and an android phone controlled with finger touch screen was used for CephNinja. Landmark identification for cephalometric analysis was carried out as demanded by the software.Statistical Analysis Used:One-way analysis of variance (ANOVA) was used for comparison between the variables, and one-way ANOVA followed by post hoc test was carried out to check the level of significance using Statistical Package for the Social Sciences software program, version 11.0.Results:The result showed that the difference of mean values obtained using the two software showed no statistical significance for 70% variables. Y-axis, incisor occlusal plane angle, and the upper incisor to A-Pog showed a statistically significant difference.Conclusion:CephNinja presented a satisfactory result with NemoCeph, and can be used interchangeably with confidence.

Compression Method in JPEG Standard

Data compression is to minimize the communication and storage. The bandwidth and storage required for a given communication system. Data compression is obviated by wideband technologies such as optical fiber communication networks, packet radio and optical storage disk. Images can generate data in such high volumes that any transmission media can be quickly overwhelmed. Personal computers and workstations has led to an increasing demand to store images in compressed from on magnetic or optical disks. The massive efforts taking place today to adopt national and international standards for signal compression. Example is the JPEG (Joint Photographic Experts Group) standard for image compression. Compression in the image from RGB in JPEG standard converted to YCbCr. Each Y, Cb, Cr divided in block 8×8 pixel. The image 8×8 pixel then transformed use DCT. Image from DCT quantized yield DC and AC component. DC component processed use DPCM (Differential Pulse Code Modulation) use Huffman table yield AC bit stream. Merge DC component and AC component yield JPEG bit stream. To get image from JPEG bit stream first use Huffman decoding. From Huffman decoding dequantized. Then transform use IDCT. Image from IDCT merge block 8×8 pixel. From block 8×8 pixel transformed YCbCr to RGB. Then get the image. Process compression is consist of lossy compression and lossless compression. Lossy compression is compression with change of data but not change meaning of information in there. Lossless compression is compression not to change data.

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.

Robust reversible data hiding scheme based on two-layer embedding strategy

Robust reversible data hiding (RRDH) prevents the hidden secret information from unintentional modifications. This paper presents a novel RRDH scheme based on two-layer embedding with reduced capacity-distortion trade-off. The proposed scheme first decomposes the image into two planes namely higher significant bit (HSB) and least significant bit (LSB) planes and then employs prediction error expansion (PEE) to embed the secret data into the HSB plane. The high correlation of HSB plane helps in achieving high embedding capacity. Further, non-malicious attacks such as Joint Photographic Experts Group (JPEG) compression which usually changes the LSBs, will not cause any disturbance to the main contents of original image and the hidden secret data. The experimental results show that the proposed scheme has superior performance than the previous works.

Reversible data hiding in JPEG images based on zero coefficients and distortion cost function

Recently, reversible data hiding (RDH) in joint photographic experts group (JPEG) images has received a great deal of attention since the JPEG image is one of the most popularly used image formats nowadays. Generally in JPEG image, the quantized discrete cosine transform (DCT) coefficients with the value of zero are far more than those with nonzero coefficients. However, most existing methods are avoided to change these zero coefficients for fear of JPEG file size increase, and the designed distortion cost functions are not perfect. In this paper, we propose a new JPEG RDH method in which the numerous zero coefficients can be modified. Based on an improved distortion cost function, a strategy which can measure the distortion for each coefficient and select coefficient positions which are most suitable for embedding is proposed. With this strategy, zero coefficients are applied to data embedding and the embedding performance is improved. Experimental results have proved that the proposed method can effectively reduce the embedding distortion and increase the embedding capacity while maintaining a relative good file size.

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.

DBFS: Dragonfly Bayes Fusion System to detect the tampered JPEG image for forensic analysis

Due to the advancement of a variety of photo editing and image processing software, image forensics analysis has become an important research topic in recent years. Numbers of research works are presented for image forensic analysis. Accordingly, this paper proposes a method named as Dragonfly Bayes Fusion System (DBFS) by integrating the Naive Bayes (NB) classifier and the Dragonfly optimization for detecting the tampered Joint Photographic Experts Group image. Initially, the input image is applied to the existing six forensic tools separately, and the classified binary map is generated. Then, the proposed DBFS fuses these decisions for generating the optimal decision. Here, the NB classifier creates the model by finding the mean and variance of every feature and this model is given as input to the Dragonfly optimization for optimally generating the probabilistic measures. Finally, the posterior probability of each feature is determined with respect to the tampered class, and the original class and the tampered image block is determined. The performance of the proposed system is evaluated with the existing methods, such as fuzzy theory based classification, rule-based classification, average method, and weighted average method for the evaluation metrics accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). The experimental results show that the proposed system outperforms the existing methods by obtaining the maximum accuracy of 0.9519, minimum FPR of 0.0490, and maximum TPR of 0.8720 when compared to the existing methods.

Impact of pre-analytical variables on deep learning accuracy in histopathology.

Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many studies irrespective of the paucity of supporting evidence. We empirically compared training image file type, training set size, and two common convolutional neural networks (CNNs) using transfer learning (ResNet50 and SqueezeNet). Thirty haematoxylin and eosin (H&E)-stained slides with carcinoma or normal tissue from three tissue types (breast, colon, and prostate) were photographed, generating 3000 partially overlapping images (1000 per tissue type). These lossless Portable Networks Graphics (PNGs) images were converted to lossy Joint Photographic Experts Group (JPG) images. Tissue type-specific binary classification ML models were developed by the use of all PNG or JPG images, and repeated with a subset of 500, 200, 100, 50, 30 and 10 images. Eleven models were generated for each tissue type, at each quantity of training images, for each file type, and for each CNN, resulting in 924 models. Internal accuracies and generalisation accuracies were compared. There was no meaningful significant difference in accuracies between PNG and JPG models. Models trained with more images did not invariably perform better. ResNet50 typically outperformed SqueezeNet. Models were generalisable within a tissue type but not across tissue types. Lossy JPG images were not inferior to lossless PNG images in our models. Large numbers of unique H&E-stained slides were not required for training optimal ML models. This reinforces the need for an evidence-based approach to best practices for histopathological ML.

Repetitive Iteration CORDIC based Energy Efficient Image Compression for Portable Devices

The rapid advancements in technology in recent years have led to a massive increase in the exchange of data (images, videos, audio, etc.) between portable devices. This has invoked the necessity for building algorithms which consume low power with no compromise in the performance. In this paper, the above captioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation, and performs equally well when compared to Joint Photographic Experts Group (JPEG) standard. Results have been obtained via Matrix Laboratory (MATLAB) and they show that the proposed technique performs equally well and consumes less power in comparison with the other techniques.

New and efficient blind detection algorithm for digital image forgery using homomorphic image processing

Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering detection algorithm for images acquired from digital cameras and scanners. The algorithm is based on applying homomorphic image processing on each suspicious image to separate illumination from reflectance components. In natural images, it is known that the illumination component is approximately constant, while changes can be detected in tampered ones. Support Vector Machine (SVM) and Neural Network (NN) classifiers are used for classification of tampered images based on the illumination component, and their results are compared to obtain the best classifier performance. The Receiver Operating Characteristic (ROC) curve is used to depict the classifier performance. Three different color coordinate systems are tested with the proposed algorithm, and their results are compared to obtain the highest accuracy level. Joint Photographic Experts Group (JPEG) compressed images with different Quality Factors (QFs) are also tested with the proposed algorithm, and the performance of the proposed algorithm in the presence of noise is studied. The performance of the SVM classifier is better than that of the NN classifier as it is more accurate and faster. A 96.93% detection accuracy has been obtained regardless of the acquisition device.

A stegano - visual cryptography technique for multimedia security

Owing to the increasing growth in digital communication as well as the multimedia applications, security has now become a very significant problem in the communication as well as storage space of such images. The Visual Cryptography (VC) has been used for hiding the information that are in the images which is a special technique of encryption that is decrypted by a human visual system. In this paper, a technique for embedding a secret message within that of a cover-image to ensure the interceptors will not observe the presence of such hidden data is presented. The method has an essential conception by means of a simple Least Significant Bit (LSB) substitution. Being inspired by the steganography approach, the current work splits the cover images into n blocks of 8X 8 pixels and into a secret message of n partitions in order to improve the image quality and to increase the capacity of secret message along with its security level. For the purpose of improving this stego-image quality and for increasing the capacity of secret message along with its security level, being inspired by the current work that splits cover images into the n blocks of 8X 8 pixels and into a secret message of n partitions. In the proposed method, the Cuckoo Search (CS) is used for searching an approximate and optimal solution of finding any optimal substitution matrix to transform the message in every block as opposed to finding a single optimal matrix for substitution and the entire cover-image is presented. The final quality of its resulting in the stego-image, and its secret message and its capacity with the level of security of this method proposed will be calculated and then comparted to the other different methods. The results of the experiment proved that the proposed method outperformed all the Joint Photographic Experts Group (the JPEG) and the Joint Quantization Table Modification (the JQTM) based method in terms of quality of image, security level and embedding capacity.

Exploration of Wireless Multimedia Sensor Network Based on Joint Photographic Experts Group Image Coding Algorithm

to study the performance of image coding and image transmission of wireless video sensor node in wireless multimedia sensor network, the challenges and design objectives of image coding technology in wireless multimedia sensor network are discussed, and the classification and exploration are carried out from two aspects: individual source coding and distributed source coding. The compression of video monitoring image sequence in wireless multimedia sensor network is studied and a low complexity image coding scheme based on change detection and improved Joint Photographic Experts Group (JPEG) algorithm is proposed. The moving target in the monitoring image is located with the change detection algorithm, that is, the area of interest. Only the region of interest is transmitted to reduce the quantity of data transmission and adapt to the limited storage and forwarding capability of wireless sensor nodes; The Discrete Cosine Transform (DCT) and quantization processes in Joint Photographic Experts Group (JPEG) algorithm are improved to reduce the computational complexity and adapt to the limited computing power of wireless sensor nodes.The analysis of algorithm complexity and the results of simulation experiment show that the proposed method can effectively reduce the data communication amount of wireless sensor nodes and the energy consumption in the calculation process while ensuring the image quality required by the application.Wireless multimedia sensor network is a new and interdisciplinary subject.The study of it is also multidirectional and multi-angle.The image coding algorithm of wireless multimedia sensor network is studied deeply.Although some achievements have been made, there are still a lot of problems that need to be further studied.

Controversial ‘pixel’ prior rule for JPEG adaptive steganography

Currently, the most successful model for image adaptive steganography is the framework of minimal distortion, in which a reasonable definition of costs can improve the security level. In the authors' previous work, they developed a rule for cost reassignment in spatial domain called the `controversial pixel prior (CPP)' rule, which defines controversial pixels by utilizing the controversies among several comparable schemes. The CPP rule gives controversial pixels higher modification priorities. In this study, they investigate migrating the CPP rule from the spatial domain to the joint photographic experts group (JPEG) domain and name it the J-CPP rule. In JPEG images, the cover elements are discrete cosine transform (DCT) coefficients and variant factors mayinfluence the distortion definition includingquantisation step, inter-blocks correlation and block energy. However, there is no evidence to reveal which factor is of highest priority for promoting security. In this work, they investigate which factor is more helpful in promoting J-CPP rule, and they finally determine to set the spatial block residual as a penalty to perfect J-CPP rule. Through extensive experiments on different JPEG steganographic algorithms and steganalysis features, they demonstrate that the J-CPP rule can improve the security of JPEG adaptive steganography.

Local Excitation Network for Restoring a JPEG-Compressed Image

Joint photographic experts group (JPEG) compression is lossy compression, and degradation of image quality worsens at high compression ratios. Therefore, a reconstruction process is required for a visually pleasant image. In this paper, we propose an end-to-end deep learning architecture for restoring JPEG images with high compression ratios. The proposed architecture changes a core principle of the squeeze and excitation network for low-level vision tasks where pixel-level accuracy is important. Instead of extracting global features, our network extracts locally embedded features and fine-tunes each feature value by using depthwise convolution. To reduce the computational complexity and parameters with large receptive fields, we use a combination of the recursive structure and feature map down- and up-scaling processes. We also propose a compact version of the proposed model by decreasing the number of filters and simplifying the network, which has about one-twentieth of the parameters of the baseline model. Experimental results reveal that our network outperforms conventional networks quantitatively, and the restored images are clear with sharp edges and smooth blocking boundaries. Furthermore, the compact model shows higher objective results while maintaining a low number of parameters. In addition, at a high compression ratio, the overall information, including details in the blocks, are lost owing to high quantization errors. We apply a generative adversarial network structure to restore these highly damaged blocks, and the results reveal that the image produced has details similar to those of the ground truth.