Joint Photographic Experts Group Research Articles

Optimized video internet of things using elliptic curve cryptography based encryption and decryption

The adaptive Joint Photographic Experts Group 2000 (JPEG2000) image compression approach employing the wavelet image transform had proposed with the rise of the optimized Video Internet of Things (VIoT) using image transmission security using Elliptic Curve Cryptography (ECC) techniques. Compressive Sensing (CS) for periodic data transfers has shown to be an excellent option for Wireless Sensor Networks (WSNs) since CS-based sensor communications drastically reduce data transmissions and enhance energy efficiency. However, another issue that arises when utilizing the optimized VIoT with image transmission compression is data loss as a result of various security risks during transmission. The numerous cooperative communication strategies proved their feasibility in different ways. However, additional issues must be handled while handling image transmission in VIoT. For example, when preparing to transmit an image, you may expend a lot of energy. The main objective of this paper is to increase image quality while minimizing processing time and error rates. However, force aptitude is vital to the research problem for the Wireless Multimedia Sensor Network (WMSN), as high-dimensional digital images use greater processing capabilities of sensor nodes. The image in WMSN is transmitted through a large number of relays. The experimental findings demonstrate the effectiveness of the suggested paradigm.

Adaptive reversible data hiding for JPEG images with multiple two-dimensional histograms

Joint photographic experts group (JPEG) can provide good quality with small file size but also eliminate extensively the redundancies of images. Therefore, hiding data into JPEG images in terms of maintaining high visual quality at small file sizes has been a great challenge for researchers. In this paper, an adaptive reversible data hiding method for JPEG images containing multiple two-dimensional (2D) histograms is proposed. Adaptability is mainly reflected in three aspects. The first one is to preferentially select sharper histograms for data embedding after K histograms are established by constructing the kth (k∈{1,2,…,K}) histogram using the kth non-zero alternating current (AC) coefficient of all the quantized discrete cosine transform blocks. On the other hand, to fully exploit the strong correlation between coefficients of one histogram, the smoothness of each coefficient is estimated by a block smoothness estimator so that a sharply-distributed 2D-histogram is constructed by combining two coefficients with similar smoothness into a pair. The pair corresponding to low complexity is selected priorly for data embedding, leading to high embedding performance while maintaining low file size. Besides, we design multiple embedding strategies to adaptively select the embedding strategy for each 2D histogram. Experimental results demonstrate that the proposed method can achieve higher rate–distortion performance which maintaining lower file storage space, compared with previous studies.

First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning.

Video surveillance systems process high volumes of image data. To enable long-term retention of recorded images and because of the data transfer limitations in geographically distributed systems, lossy compression is commonly applied to images prior to processing, but this causes a deterioration in image quality due to the removal of potentially important image details. In this paper, we investigate the impact of image compression on the performance of object detection methods based on convolutional neural networks. We focus on Joint Photographic Expert Group (JPEG) compression and thoroughly analyze a range of the performance metrics. Our experimental study, performed over a widely used object detection benchmark, assessed the robustness of nine popular object-detection deep models against varying compression characteristics. We show that our methodology can allow practitioners to establish an acceptable compression level for specific use cases; hence, it can play a key role in applications that process and store very large image data.

Reversible data hiding for JPEG images with a cascaded structure

Since Joint Photographic Experts Group is a typical image compression standard, so the reversible data hiding research on Joint Photographic Experts Group has received a lot of attention. There are two main research methods in Joint Photographic Experts Group-reversible data hiding: the method based on modifying DCT coefficients and the method based on variable length coding mapping. The disadvantage of the first one is that the visual loss is always inevitable during the DCT-coefficient modification process, while the second method often has insufficient embedding capacity and excessive file expansion. In order to solve the above problems, this paper proposes a cascaded reversible data hiding scheme, which builds a distortion minimisation scheme based on the DCT coefficients, and then cascades a file expansion suppression scheme based on the variable length coding mapping. For a particular case during the payload distribution, in this paper, all the secret information is embedded into DCT coefficients with a distortion minimisation scheme, and then the auxiliary information generated in the previous process is embedded in the variable length coding sequence with a scheme to optimise the file size expansion. The experimental results demonstrate the superiority of the proposed method in high-quality factor circumstances in terms of visual quality and file expansion.

DNA Sequence Analysis for Brain Disorder Using Deep Learning and Secure Storage

Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the image using a Deoxyribonucleic Acid (DNA) Sequence-based Joint Photographic Experts Group (JPEG) Zig Zag Encryption Algorithm (DBNJZZ). In this work, DBNJZZ implements an efficient and effective prediction model for disorders using the open-access datasets of Alzheimer's Disease Neuroimaging Initiative (Adni), the Center for Biomedical Research Excellence (Cobre), the Open Access Series of Imaging Studies (Oasis), the Function Biomedical Informatics Research Network (Fbirn), a Parkinson's dataset of 55 patients and 23 subjects with Parkinson's syndromes (Ntua), and the Parkinson's Progression Markers Initiative (Ppmi). This algorithm is implemented and tested using performance metric measures of accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). DBNJZZ gives better performance with an accuracy of 99.21% and also surpasses previous methods on other measures.

Efficient resampling features and convolution neural network model for image forgery detection

The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.

A Feature-Enriched Deep Convolutional Neural Network for JPEG Image Compression Artifacts Reduction and its Applications.

The amount of multimedia data, such as images and videos, has been increasing rapidly with the development of various imaging devices and the Internet, bringing more stress and challenges to information storage and transmission. The redundancy in images can be reduced to decrease data size via lossy compression, such as the most widely used standard Joint Photographic Experts Group (JPEG). However, the decompressed images generally suffer from various artifacts (e.g., blocking, banding, ringing, and blurring) due to the loss of information, especially at high compression ratios. This article presents a feature-enriched deep convolutional neural network for compression artifacts reduction (FeCarNet, for short). Taking the dense network as the backbone, FeCarNet enriches features to gain valuable information via introducing multi-scale dilated convolutions, along with the efficient 1 ×1 convolution for lowering both parameter complexity and computation cost. Meanwhile, to make full use of different levels of features in FeCarNet, a fusion block that consists of attention-based channel recalibration and dimension reduction is developed for local and global feature fusion. Furthermore, short and long residual connections both in the feature and pixel domains are combined to build a multi-level residual structure, thereby benefiting the network training and performance. In addition, aiming at reducing computation complexity further, pixel-shuffle-based image downsampling and upsampling layers are, respectively, arranged at the head and tail of the FeCarNet, which also enlarges the receptive field of the whole network. Experimental results show the superiority of FeCarNet over state-of-the-art compression artifacts reduction approaches in terms of both restoration capacity and model complexity. The applications of FeCarNet on several computer vision tasks, including image deblurring, edge detection, image segmentation, and object detection, demonstrate the effectiveness of FeCarNet further.

Evaluation of Compression Algorithms for 3D Pavement Image Data

A standard interchangeable data format was recently developed through the Transportation Pooled-Fund Study TPF-5(299) to meet different requirements and needs for two- and three-dimensional (2D/3D) pavement image data storage and exchange. Considering pavement images occupy large amounts of storage space, efficient compression algorithms for both 8-bit 2D and higher-bit 3D images become one of the key components for a successful implementation of the standard. Highway agencies and industry vendors exclusively use the Joint Photographic Experts Group (JPEG) or its successor JPEG 2000 standard for 2D image compression, while the compression of 3D 16-bit images remains to be proprietary. The goal of this paper is to evaluate suitable algorithms for compressing 3D pavement images. An overview of the state-of-the-art pavement image data collection technologies was performed, and the relevant image data formats were identified. Subsequently, five compression algorithms suitable for 16-bit image data were selected based on results from a comprehensive survey of the TPF-5(299) technical community. The compressions of several representative field images were implemented and evaluated in terms of the computation efficiency and retaining of image quality. The expected benefits of this study include facilitating standard and efficient pavement image data collection and accelerating the development of consistent pavement condition evaluation.

Self embedding digital watermark using hybrid method against compression attack

<p>In the modern time interacting with digital world become standard life activity, human need a way to protect properties as individuals or corporals, and we do that by embedding a digital mark to the target, and this technique call digital watermarking. But there still is a chance to manipulate or even remove this marks we embed for protection with various attacks like adding noises, compression-decompression or bits manipulations, and that why companions, individuals, laboratories are still developing new methods to embed this marks and make them more robust and more hard to detect for others. There are so many methods for digital watermarking, so we chose the least significant bits watermarking (LSB-watermarking) to provide an invisible digital watermarking, and on top of that we proceed with the blind LSB-watermarking method so that we don't get bind to the original image, and for our attack we chose compression joint photographic experts group (JPEG) compression because it’s the most used method for image and videos compression along with singular value decomposition (SVD) to make our mark as robust as possible. And the results we gain from our method are promising and it did give as high quality digital watermarking.</p>

Blind robust image watermarking based on adaptive embedding strength and distribution of quantified coefficients

The image watermarking technique is a reliable method for protecting digital image rights. The most widely used digital imaging format is the JPEG (joint photographic expert’s group). This paper proposes a novel reversible robust watermarking scheme based on the JPEG image. In JPEG image, quantified Discrete Cosine Transform (DCT) coefficients with varying frequency provide varied capacities and embedding deformities. To minimize total distortion for the watermarked image, we adaptively select embedding strengths and coefficients from effective frequency selection that produce less embedding distortions. Then, by weighting the difference between the quantified DCT coefficient and the DCT coefficient on the block, an advanced block selection strategy is used to the corresponding distortion, which are produced from the whole quantified alternating current (AC) shift capacity and half of the insertion capacity. The experimental results show that the proposed method can maintain good visual quality while requiring little bitstream expansion and is robust to geometrical and image processing attacks.

An entropy minimization histogram mergence scheme and its application in image compression

In this paper, we propose an entropy minimization histogram mergence (EMHM) scheme that can significantly reduce the number of grayscales with nonzero pixel populations (GSNPP) without visible loss to image quality. We proved in theory that the entropy of an image is reduced after histogram mergence and that the reduction in entropy is maximized using our EMHM. The reduction in image entropy is good for entropy encoding considering that the minimum average code word length per source symbol is the entropy of the source signal according to Shannon’s first theorem. Extensive experimental results show that our EMHM can significantly reduce the code length of entropy coding, such as Huffman, Shannon, and arithmetic coding, by over 20% while preserving the image subjective and objective quality very well. Moreover, the performance of some classic lossy image compression techniques, such as the Joint Photographic Experts Group (JPEG), JPEG2000, and Better Portable Graphics (BPG), can be improved by preprocessing images using our EMHM.

JPEG XS—A New Standard for Visually Lossless Low-Latency Lightweight Image Coding

Joint Photographic Experts Group (JPEG) XS is a new International Standard from the JPEG Committee (formally known as ISO/International Electrotechnical Commission (IEC) JTC1/SC29/WG1). It defines an interoperable, visually lossless low-latency lightweight image coding that can be used for mezzanine compression within any AV market. Among the targeted use cases, one can cite video transport over professional video links (serial digital interface (SDI), internet protocol (IP), and Ethernet), real-time video storage, memory buffers, omnidirectional video capture and rendering, and sensor compression (for example, in cameras and the automotive industry). The core coding system is composed of an optional color transform, a wavelet transform, and a novel entropy encoder, processing groups of coefficients by coding their magnitude level and packing the magnitude refinement. Such a design allows for visually transparent quality at moderate compression ratios, scalable end-to-end latency that ranges from less than one line to a maximum of 32 lines of the image, and a low-complexity real-time implementation in application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), central processing unit (CPU), and graphics processing unit (GPU). This article details the key features of this new standard and the profiles and formats that have been defined so far for the various applications. It also gives a technical description of the core coding system. Finally, the latest performance evaluation results of recent implementations of the standard are presented, followed by the current status of the ongoing standardization process and future milestones.

A focus measure in discrete cosine transform domain for multi-focus image fast fusion

Under the Joint Photographic Experts Group (JPEG) framework, this paper proposes a simple yet efficient focus measure (FM) in discrete cosine transform (DCT) domain for fast multi-focus image fusion. At first, the DCT coefficients of multi-focus source images in JPEG format are read directly. Then, the initial decision map is constructed according to the proposed FM using only a few low-order DCT coefficients. Finally, the final decision map is refined with morphological operation, and the fused image is achieved by a simple fusion rule. The experimental results demonstrate that the proposed method takes at least 1% less fusion time than the existing fastest methods by neural network while ensuring the fusion effect in both visual perception and objective evaluations. Therefore, the proposed method is more suitable for wireless visual sensor network (WVSN) nodes with edge computing than the state-of-the-art methods.

Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

A Hidden DCT-Based Invisible Watermarking Method for Low-Cost Hardware Implementations

This paper presents an invisible and robust watermarking method and its hardware implementation. The proposed architecture is based on the discrete cosine transform (DCT) algorithm. Novel techniques are applied as well to reduce the computational cost of DCT and color space conversion to achieve low-cost and high-speed performance. Besides, a watermark embedder and a blind extractor are implemented in the same circuit using a resource-sharing method. Our approach is compatible with various watermarking embedding ratios, such as 1/16 and 1/64, with a PSNR of over 45 and the NC value of 1. After Joint Photographic Experts Group (JPEG) compression with a quality factor (QF) of 50, our method can achieve an NC value of 0.99. Results from a design compiler (DC) with TSMC-90 nm CMOS technology show that our design can achieve the frequency of 2.32 GHz with the area consumption of 304,980.08 μm2 and power consumption of 508.1835 mW. For the FPGA implementation, our method achieved a frequency of 421.94 MHz. Compared with the state-of-the-art works, our design improved the frequency by 4.26 times, saved 90.2% on area and increased the power efficiency by more than 1000 fold.

Automatic classification of medical image modality and anatomical location using convolutional neural network.

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space.

Comparative Study on MATLAB based Joint Photographic Experts Group Image Size Reduction Using Shearlet and Wavelet Packet Transform for X-ray Images with Potential Hospital Data Storage Applications

Aim: Aim of this study is to compress X-ray images using wavelet packet transform and shearlet transform. These two transforms were compared for the better compression ratio to store large hospital data files in small memory space. Materials and methods: Sample X-ray images were, Wavelet packet (30) and Shearlet (30) for compression. Compression ratio was calculated. The significance of the data were calculated using SPSS software. Results: There was statistical significance between the wavelet packet and shearlet transform based compression ratio data (p<0.05, independent sample t- test). Conclusion: wavelet packet transform has a better compression ratio than shearlet transform. Wavelet packet transform appears to produce the most consistent results with higher standard deviation (3.3) when compared with shearlet transform which appears to produce lower results with lower standard deviation (0.8).

Comparative Study on MATLAB based Joint Photographic Experts Group Image Size Reduction Using Shearlet and Wavelet Transform for X-Ray Images with Potential Hospital Data Storage Application

Aim: The aim of this paper is to compress the x-ray images using wavelet and shearlet transform and compare the compression ratios to find a better transform. Materials and Methods: The sample size of each group was 30 and the total sample size was 60. Compression ratios (CR) were generated for images based on the transform used for size reduction. Results: Wavelet based CR was seen to be higher mean (6.61) when compared to shearlet (4.025). Hence wavelet transform seems better transform for compressing X-Ray images. Conclusion: We observed that wavelet transform seems to have a better compression ratio compared to shearlet transform (P=0.28, P<0.05, independent sample t-test).

Automated Identification Model of Ground-Glass Opacity in CT-Scan Image by COVID-19

Ground-Glass Opacity (GGO) is an object found in the thorax due to infection. This object interferes with the normal function of the thorax in breathing. The characteristic of GGO has slightly lighter turbidity compared to normal thorax tissue on radiological images, so it is very difficult to identify it precisely. This study aims to identify the GGO pattern and find the exact area of the CT-scan image of COVID-19 sufferers. The data tested were 34 images from 34 different patients. The image was taken using CT-Scan equipment with the tube model 46274891G1 axially. Each patient is taken one image with the reading position right above the chest using the file format Joint Photographic Experts Group (jpg). An automatic image processing model developed in this study uses several interrelated and continuous technical steps; Image Enhancement, Convert to Binary Image, Morphology Operation, Image Inverted, Active Contour Model, Image Addition, Convert Matrix to Grayscale, Image Filtering, Convert to Binary Image, Image Subtraction and Region Properties. The results of this study can identify GGO in all patient test images, where each patient has GGO. The smallest area of GGO was 3.9%, and the highest was 34.2% of the total thorax area. This level of comparison is greatly influenced by the severity of the COVID-19 virus patient. This area of GGO weakens the normal function of the thorax in the respiratory process of the patient. Thus, this research can be used as a model recommendation in identifying thorax damage due to COVID-19 very well in following up on more intensive treatment in the future.

Shaping duo binary turbo-coded BICM scheme for JPWL image transmission using a link adaptation strategy over wireless channels

In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions.