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Recently, 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.

First, 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.

Experimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.

To verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.

This 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.

With the advent of technology, a huge amount of data is being transmitted and received through the internet. Large bandwidth and storage are required for the exchange of data and storage, respectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. The main purpose of the proposed system is to use the bandwidth effectively. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. Compression techniques even help for smaller storage requirements.

The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. The proposed method is simple and reduces the complexity of the convolutional techniques.

Coding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen.

With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB. The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues.

The interdisciplinary nature of video networking, coupled with various recent developments in standards, proposals and applications, poses great challenges to the research and industrial communities working in this area. The main purpose of this paper is to provide a tutorial and survey on recent advances in video networking from an integrated perspective of both video signal processing and networking.

Detailed technical descriptions and insightful analysis are presented for recent and emerging video coding standards, in particular the H.264 family. The applications of selected video coding standards in emerging wireless networks are then introduced with an emphasis on scalable video streaming in multihomed mobile networks. Both research challenges and potential solutions are discussed along the description, and numerical results through simulations or experiments are provided to reveal the performances of selected coding standards and networking algorithms.

The tutorial helps to clarify the similarities and differences among the considered standards and networking applications. A number of research trends and challenges are identified, and selected promising solutions are discussed. This practice would provoke further thoughts on the development of this area and open up more research and application opportunities.

Not all the concerned video coding standards are complemented with thorough studies of networking application scenarios.

The discussed video coding standards are either playing or going to play indispensable roles in the video industry; the introduced networking scenarios bring together these standards and various emerging wireless networking paradigms towards innovative application scenarios.

The comprehensive overview and critiques on existing standards and application approaches offer a valuable reference for researchers and system developers in related research and industrial communities.

High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications.

In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME.

The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827.

In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The purpose of this paper is to select the best scaling coefficient during the quantitative-qualitative conversion.

Cloud model can describe the qualitative concept of randomness and fuzziness, achieve uncertain transition between qualitative and quantitative in the field of multi-criteria group decision and has been receiving widespread attention. This paper discusses scale conversion issues of the cloud model when evaluating qualitative information. In order to improve the accuracy of the evaluation on multi-attribute decision problems based on uncertainty of natural linguistic information, this paper proposes a method of self-testing cloud model based on a composite scale (with the exponential scale and the scale as a basis).

Through experimental verification results show that under composite scale, the best suitable selection of can effectively improve the accuracy and reliability of decision results.

This research presents a new approach to determine the suitable value for coefficient based on uncertain knowledge of natural multi-criteria group decision making, and gives concrete steps and examples. This method has positive significance to improve the quality of qualitative and quantitative conversion based on cloud model.

The joint photographic experts group (JPEG) 2000 image compression system is being used for cultural heritage preservation. The authors are aware of over a dozen of big memory institutions worldwide using this format. This paper aims to review and explain choices for end users to help resolve trade-offs that these users are likely to encounter in practice.

The JPEG 2000 format is quite complex and therefore sometimes considered as a preservation risk. A lossy compression is governed by a number of parameters that control compression speed and rate-distortion trade-off. Their inappropriate adjustment may fairly easily lead to sub-optimal compression performance. This paper provides general guidelines for selecting the most appropriate parameters for a specific application.

This paper serves as a guide for the preservation of digital heritage in cultural heritage institutions, including libraries, archives and museums.

This paper serves as a guide for the preservation of digital heritage in cultural heritage institutions, including libraries, archives and museums.

The purpose of this paper is to develop a new criterion for the exponential stability and

_∞ performance of state-space digital filters under the influence of any combination of quantization/overflow nonlinearities.

The proposed criterion uses the

_∞ approach that is suitable for the design of discrete system in the presence of external disturbance. Analysis and synthesis in an

_∞ setting is advantageous as it proposes effective disturbance attenuation, less sensitivity to uncertainties and many practical applications.

The criterion not only guarantees exponential stability but also reduces the effect of external interference. A numerical example demonstrating the effectiveness of the proposed method is given.

The main result of the paper is reported for the first time and it is useful to ensure the stability of digital filters in the presence of external disturbance and any combination of quantization/overflow nonlinearities.

This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.

The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.

This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.

The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization.

In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter.

A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking.

In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.