Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue
{"title":"Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images","authors":"Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue","doi":"10.1155/2021/5519508","DOIUrl":"https://doi.org/10.1155/2021/5519508","url":null,"abstract":"The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared images. In the first stage of the generative adversarial network, it focuses on recovering the overall contour information of the image to obtain clear image edges; the second stage of the generative adversarial network focuses on recovering the detailed feature information of the image and has a stronger ability to express details. The infrared image superresolution reconstruction method proposed in this work has highly realistic visual effects and good objective quality evaluation results.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"47 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120872976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multibody Nonrigid Structure from Motion Segmentation Based on Sparse Subspace Clustering","authors":"Wenqing Huang, Qingfeng Hu, Yaming Wang, M. Jiang","doi":"10.1155/2021/6686179","DOIUrl":"https://doi.org/10.1155/2021/6686179","url":null,"abstract":"Sparse subspace clustering (SSC) is one of the latest methods of dividing data points into subspace joints, which has a strong theoretical guarantee. However, affine matrix learning is not very effective for segmenting multibody nonrigid structure from motion. To improve the segmentation performance and efficiency of the SSC algorithm in segmenting multiple nonrigid motions, we propose an algorithm that deploys the hierarchical clustering to discover the inner connection of data and represents the entire sequence using some of trajectories (in this paper, we refer to these trajectories as the set of anchor trajectories). Only the corresponding positions of the anchor trajectories have nonzero weights. Furthermore, in order to improve the affinity coefficient and strong connection between trajectories in the same subspace, we optimise the weight matrix by integrating the multilayer graphs and good neighbors. The experiments prove that our methods are effective.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123264330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography","authors":"R. Ogundokun, O. Abikoye","doi":"10.1155/2021/8827055","DOIUrl":"https://doi.org/10.1155/2021/8827055","url":null,"abstract":"Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124765339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Crowd Motion Editing Based on Mesh Deformation","authors":"Yong Zhang, Xinyu Zhang, Tao Zhang, Baocai Yin","doi":"10.1155/2020/3634054","DOIUrl":"https://doi.org/10.1155/2020/3634054","url":null,"abstract":"Computer simulation is a significant technology on making great scenes of crowd in the film industry. However, current animation making process of crowdmotion requires large manual operations which are time-consuming and inconvenient. To solve the above problem, this paper presents an editing method on the basis of mesh deformation that can rapidly and intuitively edit crowd movement trajectories from the perspective of time and space. The method is applied to directly generate and adjust the crowd movement as well as avoid the crash between crowd and obstacles. As for collisions within the crowd that come along with path modification problem, a time-based solution is put forward to avoid this situation by retaining relative positions of individuals. Moreover, an experiment based on a real venue was performed and the result indicates that the proposed method can not only simplify the editing operations but also improve the efficiency of crowd motion editing.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114899065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Capacity Improvement for DVB-NGH with Dual-Polarized MIMO Spatial Multiplexing and Hybrid Beamforming","authors":"H. Nguyen, Trung-Tan Le, Trung-Hien Nguyen","doi":"10.1155/2020/9578521","DOIUrl":"https://doi.org/10.1155/2020/9578521","url":null,"abstract":"Multiple-input multiple-output (MIMO) antenna scheme is an effective technique for future terrestrial broadcasting systems such as digital video broadcasting-next-generation handheld (DVB-NGH) to overcome the limits on information theory of traditional single-antenna wireless communications without any additional bandwidth or increased transmit power. In this paper, we propose a hybrid beamforming scheme for dual-polarized MIMO spatial multiplexing DVB-NGH broadcasting systems. The system of interest makes use of phase shifters and amplitude attenuators for the digital-analog precoder at beamforming stage of the transmitter end to maximize the signal-to-noise ratio to increase the channel capacity of the DVB-NGH systems. At the receiver end, the maximum likelihood (ML) detector is used to evaluate the system performance. We consider the signal-to-interference-and-noise ratio (SINR) and the achievable average capacity for the DVB-NGH MIMO with various FFT sizes, number of transmit antennas, and different modulation schemes. The performance results on bit error rate, channel capacity, and beampatterns show that the proposed hybrid beamforming and dual-polarized MIMO spatial multiplexing schemes provide more robustness against signal interference by beamforming and/or nulling techniques. The simulation results also show that the proposed system achieves higher capacity than the existing MIMO schemes for the DVB-NGH systems.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123774589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum","authors":"Guangliang Pan, Jun Li, Fei Lin","doi":"10.1155/2020/5069021","DOIUrl":"https://doi.org/10.1155/2020/5069021","url":null,"abstract":"In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123286201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yongchao Ye, Lingjie Lao, Diqun Yan, Rangding Wang
{"title":"Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network","authors":"Yongchao Ye, Lingjie Lao, Diqun Yan, Rangding Wang","doi":"10.1155/2020/8927031","DOIUrl":"https://doi.org/10.1155/2020/8927031","url":null,"abstract":"Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to be abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for weakly pitch-shifted voice. In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitch-shifted voice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. Specifically, linear frequency cepstral coefficients (LFCC) computed from power spectrums are considered and their dynamic coefficients are extracted as the discriminative features. And the CNN model is carefully designed with particular attention to the input feature map, the activation function and the network topology. We evaluated the algorithm on voices from two datasets with three pitch shifting software. Extensive results show that the algorithm achieves high detection rates for both binary and multiple classifications.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125725609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Subjective Evaluation of Music Compressed with the ACER Codec Compared to AAC, MP3, and Uncompressed PCM","authors":"Stuart Cunningham, I. Mcgregor","doi":"10.1155/2019/8265301","DOIUrl":"https://doi.org/10.1155/2019/8265301","url":null,"abstract":"Audio data compression has revolutionised the way in which the music industry and musicians sell and distribute their products. Our previous research presented a novel codec named ACER (Audio Compression Exploiting Repetition), which achieves data reduction by exploiting irrelevancy and redundancy in musical structure whilst generally maintaining acceptable levels of noise and distortion in objective evaluations. However, previous work did not evaluate ACER using subjective listening tests, leaving a gap to demonstrate its applicability under human audio perception tests. In this paper, we present a double-blind listening test that was conducted with a range of listeners (N=100). The aim was to determine the efficacy of the ACER codec, in terms of perceptible noise and spatial distortion artefacts, against de facto standards for audio data compression and an uncompressed reference. Results show that participants reported no perceived differences between the uncompressed, MP3, AAC, ACER high quality, and ACER medium quality compressed audio in terms of noise and distortions but that the ACER low quality format was perceived as being of lower quality. However, in terms of participants’ perceptions of the stereo field, all formats under test performed as well as each other, with no statistically significant differences. A qualitative, thematic analysis of listeners’ feedback revealed that the noise artefacts that produced the ACER technique are different from those of comparator codecs, reflecting its novel approach. Results show that the quality of contemporary audio compression systems has reached a stage where their performance is perceived to be as good as uncompressed audio. The ACER format is able to compete as an alternative, with results showing a preference for the ACER medium quality versions over WAV, MP3, and AAC. The ACER process itself is viable on its own or in conjunction with techniques such as MP3 and AAC.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131193027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Projection Analysis Optimization for Human Transition Motion Estimation","authors":"Wanyi Li, Feifei Zhang, Qiang Chen, Q. Zhang","doi":"10.1155/2019/6816453","DOIUrl":"https://doi.org/10.1155/2019/6816453","url":null,"abstract":"It is a difficult task to estimate the human transition motion without the specialized software. The 3-dimensional (3D) human motion animation is widely used in video game, movie, and so on. When making the animation, human transition motion is necessary. If there is a method that can generate the transition motion, the making time will cost less and the working efficiency will be improved. Thus a new method called latent space optimization based on projection analysis (LSOPA) is proposed to estimate the human transition motion. LSOPA is carried out under the assistance of Gaussian process dynamical models (GPDM); it builds the object function to optimize the data in the low dimensional (LD) space, and the optimized data in LD space will be obtained to generate the human transition motion. The LSOPA can make the GPDM learn the high dimensional (HD) data to estimate the needed transition motion. The excellent performance of LSOPA will be tested by the experiments.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115708423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Peer-Assisted Content Delivery to Reduce the Bandwidth of TSTV Service in IPTV System","authors":"El Hassane Khabbiza, R. E. Alami, H. Qjidaa","doi":"10.1155/2019/2495310","DOIUrl":"https://doi.org/10.1155/2019/2495310","url":null,"abstract":"With great technologies, emerge new needs and requirements. The progress achieved in Access broadband rates accentuated the demand on IPTV (Internet Protocol Television) services specially Video on Demand (VOD) and Time Shift TV (TSTV) which led subsequently to a great need for efficiency in the use of bandwidth consumed by those services. Optimization solutions are considered by IPTV service providers to lighten the service load especially on Access Nodes uplinks. This paper describes an optimization of TSTV dedicated bandwidth based on a peer-assisting TSTV content delivery, a solution in which the users STBs (set-top-boxes) assist the central TSTV servers in the service fulfillment. For this purpose, for each TSTV request, the STB will be receiving the TSTV stream from a neighbor STB instead of the central server. By using this method, the unicast traffic will not pass through the IP network; it will be a peer-to-peer communication within the access network. Extensive simulation results were included to illustrate the validity of the proposed new solution.","PeriodicalId":204253,"journal":{"name":"Int. J. Digit. Multim. Broadcast.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}