International Journal of Digital Multimedia Broadcasting

Artistic image transformation is a computer technique widely applied in art creation, design, entertainment, and cultural heritage by converting images into artistic styles. It offers innovative ways for artists to express themselves, provides designers with more choices and inspiration, enhances visual esthetics, and enables creative implementations in movies, games, and virtual reality. Additionally, it aids in the restoration and preservation of ancient artworks, allowing a deeper appreciation of classical art. Traditional image transformation methods, though effective for simple effects, lack the flexibility and expressiveness of deep learning–based approaches. To enhance the effectiveness and efficiency of artistic image transformation, this paper employs generative adversarial networks (GANs), which utilize an adversarial training mechanism between a generator and a discriminator to produce high-quality and realistic image transformations. This study introduces spectral normalization (SNGAN) to further improve GAN performance by constraining the spectral norm of the discriminator’s weight matrix, preventing gradient issues during training, thus improving convergence and image quality. Experimental results on the CHAOS dataset indicate that the proposed SNGAN model achieves the lowest mean absolute error (MAE) of 0.3420, the highest peak signal-to-noise ratio (PSNR) of 32.1423, and a structural similarity index (SSIM) of 0.6696, closely matching the best result. Additionally, the SNGAN model demonstrates the shortest training time, highlighting its efficiency. These results confirm that the proposed method achieves more realistic and efficient artistic image transformations compared to traditional methods and other deep learning algorithms.

Nowadays, the strong development of the economy and society has driven the increase in traffic participation, making traffic management increasingly difficult. To effectively address this issue, AI applications are being applied to improve urban traffic management and operations. Therefore, we propose a smart system to detect and monitor vehicles across multiple surveillance cameras. Our system leverages data collected from traffic surveillance cameras and harnesses the power of deep learning technology to detect and track vehicles smoothly. To achieve this, we use the YOLO model for detection in conjunction with the DeepSORT algorithm for precise vehicle tracking on each camera. Furthermore, our system uses a ResNet backbone model for feature extraction of objects within each camera’s frame. It utilizes cosine distance to identify similar objects in other cameras, facilitating multicamera tracking. To ensure optimal performance, our system is implemented using the NVIDIA DeepStream SDK, enabling it to achieve an impressive speed of 21 fps on each camera and an average of precision approximately 85% for three modules. The results of our study affirm the system’s suitability and its potential for practical applications in the field of urban traffic management.

Conventional data acquisition systems face challenges in achieving high acquisition speeds and rapid storage of large data volumes using microcontrollers. In contrast, field-programmable gate arrays (FPGAs) offer numerous advantages, including high clock frequencies, minimal internal delays, fast operational speeds, abundant internal RAM resources, and simplified control of complex peripheral circuits. This study presents the design of an FPGA-based multimedia remote monitoring system for information technology server rooms. The proposed system utilizes an FPGA as the primary controller and incorporates environmental sensors, electrical energy sensors, carbon monoxide sensors, smoke sensors, and A/D converter modules to monitor multiple locations within the server room. Simultaneously, the FPGA transmits the collected data from each monitoring point via a serial port to an LCD serial screen for display. An alarm is triggered if any environmental anomalies are detected, indicating abnormal statuses. Additionally, the system employs the fast Fourier transform algorithm and butterfly operations to derive voltage and current AC quantities, while utilizing relative temperature differences to identify equipment faults within the server room. The system is evaluated in terms of functionality, user-friendliness, and reliability. Experimental results demonstrate that all performance measures align with expectations, fulfilling the initial design objectives and highlighting the potential applicability and relevance of FPGA technology in the monitoring field. In addition, a comparison between FPGA and traditional microcontroller systems is performed, showcasing the superior processing speed and performance of the FPGA-based system. This comparative analysis further validates the advantages of using FPGA technology in high-speed data acquisition and monitoring applications.

Digital technology offers numerous advantages, such as preserving the authenticity, replicating reality, and facilitating dissemination. It enables the preservation of intangible cultural heritage (ICH) in its original form and allows for the creation of comprehensive graphic, audio, and visual databases. Among these technologies, holographic technology holds promise for protecting ICH and promoting its dissemination. This paper focuses on interactive holographic technology and presents the design and implementation of a dynamic holographic display system that combines digital hologram (DH) and computer-generated hologram (CGH) to showcase 3D images consisting of both virtual and real objects. Real-time loading of DH into a spatial light modulator enables the optical reproduction of real objects, while the loading of two CGHs into other spatial light modulators facilitates the optical reproduction of virtual objects. Computational holography allows for the addition of virtual information, such as coordinate text, and the fusion of the three reconstructed images in space, resulting in an augmented reality experience and enhanced 3D display of real objects. An experimental setup employing three liquid crystal on silicon (LCOS) devices confirms the validity of the proposed method. Compared to other techniques, this approach demonstrates improved image signal-to-noise ratio, reduced alignment errors, and wider coverage of light traversal for laser 3D reconstruction images. The holographic technology presented in this paper enables the fusion display of real and virtual scenes and real-time two-way interaction between the audience and virtual images. This research holds significant practical value in promoting the effective dissemination and protection of ICH.

In the context of the rapid development of multimedia and information technology, machine translation plays an indispensable role in cross-border e-commerce between China and Japan. However, due to the complexity and diversity of natural languages, a single neural machine translation model tends to fall into local optimality, leading to poor accuracy. To solve this problem, this paper proposes a general multimodal machine translation model based on visual information. First, visual information and text information are used to generate a visual representation of perceptual text information. Then, the two modal information are encoded separately, and the proportion of visual information in the whole multimodal information is controlled by a gating network. Finally, experiments are conducted on the image description datasets MSCOCO, Flickr30k, and video dataset VATEX, respectively. The results show that the algorithm in this paper achieves the best performance on both the BLEU and METEOR evaluation metrics.

With the development of film and television industry and the rise of new media short video, film and television postproduction process has higher and higher requirements for image quality. In film and television postproduction, optimizing the image quality can enhance the resolution and make the image more vivid and detailed. High-quality image can fully embody the value of film and television, as well as promote the development of new media short videos. This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. This technology can enhance the ability to extract image features, improve the accuracy of image feature extraction, and then improve the image resolution and details, thus improving the image quality in the process of film and television postproduction. The results show that the average value of PSNR is 30.29. The average value of PSNR of the proposed algorithm is higher than that of other algorithms, indicating that the error between the image processed by the proposed algorithm and the original image is small. The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. Compared with other algorithms, the structure processed by the algorithm in this paper is more similar to the original structure, resulting in a better graph. The algorithm in this paper has the best performance on both the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM). The improved image processing technology proposed in this paper can effectively improve the accuracy of image feature extraction, making film and television or new media short video images of higher quality.

The digital multimedia network data traffic is huge, and many end devices are applied to the digital multimedia network; therefore, the digital media network communication delay transmission must be analysed in order to improve the performance of the digital multimedia network communication transmission. Due to the presence of a large number of data packets in the communication network used, some random delays in data can occur during use. Conventional data transmission methods rely solely on extended data storage capacity to achieve this, making it difficult to achieve intelligent scheduling, resulting in delays and optimized performance degradation during communication data transmission. The aim of this paper is to enhance the communication quality of digital multimedia networks by developing an algorithmic model that rapidly mitigates communication delays. The proposed approach employs a delay cancellation algorithm in a network predictive control system. Simulation techniques are utilized to simulate and compensate for delays in multimedia communication networks. The efficacy of the network control strategy is demonstrated through simulation tests on a large-scale multimedia network communication system. The research findings indicate that the network control prediction model and elimination algorithm can substantially reduce the packet loss rate and quickly eliminate communication delays in digital multimedia networks.

In order to solve the bottleneck problem of public service advertising, an analysis method of creative design and design and production technology of animation public service advertising based on the ant colony optimization algorithm was proposed. In the current creative design process of animation public service advertising, along with the improvement of people’s aesthetic requirements, it is necessary to carry out a comprehensive innovative design of its design methods and content. This paper mainly describes how to use the ant colony optimization algorithm in the actual design process to carry out the corresponding design and analysis and then improve the overall communication of advertising. The research results show that under the ant colony algorithm, the customer satisfaction rate of public service advertising design is 89%, the general rate is 10%, and the dissatisfaction rate is 1%, which is superior to the traditional algorithm. Using Flash, 3DS Max, Premiere, and other software as well as “3D simulation,” Easy Mocap motion capture, and other technologies, the production quality of animation public service advertising films has been effectively improved.

Postgraduates being as the backbone of the innovative talent team, the enhancement of postgraduates’ innovative behavior has become the focus of higher education reform in the new era. Postgraduate students’ innovative behaviors are influenced by multiple factors, and mentorship plays a crucial role in the cultivation of postgraduate students’ innovative behaviors. This study is based on social cognitive theory and empirical analysis from the perspective of individual postgraduate students and mentorship cultivation. Multisource data were obtained from a research team attending a teacher training college in southwest China, and a questionnaire survey was conducted on 362 postgraduate students based on an online approach. The empirical study was conducted using SPSS software and AMOS software combined with hierarchical regression analysis and structural analysis of covariance to examine the mechanism of the effect of transformational tutoring style on postgraduate students’ innovative behavior, using creative self-efficacy as a mediating variable. The results showed that the transformational tutoring style had a significant positive effect on the innovation behavior of postgraduate students, and the creative self-efficacy partially mediated the effect of the transformational tutoring style on the innovation behavior. According to the findings of the study, the creative self-efficacy of postgraduate students is enhanced through the collaboration of “multiple” subjects; the “integrated” cultivation model is built to create a transformative tutor team; a mentoring community is established. The study is aimed at providing a reference for the cultivation of innovation ability of master students.

With the change of the times, under the leadership of big data, cloud computing, network technology, mobile Internet, and other high technology, education has gradually broken free from the traditional teaching mode. At the same time, advanced technology has also lifted the confinement of traditional teaching mode in space and time and opened the skylight of intelligent thinking mode. From the current situation of Chinese language fever around the world, the demand of Chinese language learners for learning materials and learning resources will continue to increase in the future. With the continuous development of Internet education, the combination of Internet technology and international Chinese education will definitely become the key direction for the development of international Chinese education. In this context, online teaching resources will become an important basis for the development of “Internet+Chinese international education”, so it is necessary to investigate and study the online teaching resources of “Internet+Chinese international education.” As a matter of fact, with the development of society and the advancement of technology, the era of informationization has come. As a result, the main theme of education in the information age is to provide suitable education for students who want to learn Chinese and to promote the active and lively development of each student. In the past, the one-size-fits-all education model of classroom teaching could not meet the individual development of students and the demand of society for diversified talents. Therefore, the traditional teaching of Chinese as a foreign language is in urgent need of change, and personalized teaching is attracting attention. With the emergence of technologies such as cloud computing, Internet of Things, and big data in education, personalized teaching has received technical support. This paper is aimed at exploring how to apply the new technologies in the teaching process to help teachers personalize teaching, stimulate students’ interest in learning, meet their individual needs, break through the traditional teaching methods, and make it possible to teach according to their abilities. As one of the main learning resources, the quality of multimedia courseware will be the criterion to measure whether it meets the teaching and learning needs. Consequently, the use of big data, multimedia, and multimodal technologies to improve the Chinese language material library and develop special software for teaching Chinese as a foreign language, as well as the production of high-quality multimedia courseware that is highly compatible with the teaching materials, will become the trend of future research.

Virtual reality technology (VR) has been widely used in education and teaching, but the results of a large number of experimental studies on the impact of VR on students’ technical skill learning are not consistent. In this study, a meta-analysis method was used to conduct in-depth quantitative analysis of 32 experimental or quasi-experimental research literatures based on virtual reality in the past 20 years. The results showed that: (1) virtual reality technology has a moderately positive effect on the learning of technical skills of science and engineering college students ( SMD = 0.363 ); (2) the effect of VR on cognitive strategies ( SMD = 0.487 ) was significantly greater than that of mental skills ( SMD = 0.275 ) for technical skills under different measurement dimensions. (3) Different test periods, previous experience levels, and auxiliary methods have different degrees of positive influence on improving students’ technical skills, among which the 40-60 minutes experiment period ( SMD = 0.484 ) has the greatest influence on the improvement of technical skills. According to the research conclusions, we suggest to make an overall planning for the construction of VR enabling teaching application scenarios and create a human-machine collaborative interactive teaching model of “theory+VR simulation+feedback” based on flow theory.

Compression is an essential process to reduce the amount of information by reducing the number of bits, this process is necessary for uploading images, audio, video, storage services, and TV transmission. In this paper, Image compressions with losses from this action will be shown for some common patterns. The compression process uses different mathematical equations that have different methods and efficiencies, so some common mathematical methods for each style are presented taking into consideration the pros and cons of each method. In this paper, it is demonstrated that there is a quality improvement by applying anisotropic interpolation to edge enhancement for its ability to satisfy the dispersed data of the propagation process, which leads to faster compression due to concern for optimum quality rather than fast algorithms. The test images for these patterns showed a discrepancy in the image resolution when the compression coefficient was increased, as the results using three types of image compression methods proved a clear superiority when using "Partial Differential Equations (PDE)".

After cataract, glaucoma is one of the second leading retinal diseases in the world. This paper presents the methodology to detect the glaucoma using principal component analysis. The images are involved in dilation as a preprocessing, enhancement using the contrast limited adaptive histogram equalization method, and followed by the extraction of features using principal component analysis. The extracted features are classified using support vector machine, Naive Bayes, and K-nearest neighbor classifiers. Comparing with other classifiers, the Naive Bayes provides high accuracy of 95% which demonstrates the effectiveness of the feature extraction and the classifier.

Academic procrastination is a common problem among current master’s degree students in their research activities and is an important factor that hinders academic progress. Based on the cognitive theory perspective, this study conducted a web-based questionnaire survey on full-time master’s students enrolled in a teacher training university in southwest China. The data collected were analyzed by using SPSS software with hierarchical regression and PROCESS macroprogramming techniques to investigate the relationship between stress perception and academic procrastination and the moderating effects of self-regulated learning efficacy and self-control among master’s students. The study showed that stress perception was a significant positive predictor of academic procrastination, and both self-regulated learning efficacy and self-control played a moderating role in the relationship between stress perception and academic procrastination. The study suggests that, firstly, to stimulate the personal potential of graduate students with reasonable perception of stress; secondly, to enhance the action ability of graduate students by strengthening metacognitive strategies; finally, to establish a two-way promotion mechanism by enhancing self-regulation and impulse control.

To solve the limitations of complexity and repeatability of existing broadcast filming systems, a new broadcast filming system was developed. In particular, for Korean music broadcasts, the shooting sequence is stage and lighting installation, rehearsal, lighting effect production, and main shooting; however, this sequence is complex and involves multiple people. We developed an automatic shooting system that can produce the same effect as the sequence with a minimum number of people as the era of un-tact has emerged because of COVID-19. The developed system comprises a simulator. After developing a stage using the simulator, during rehearsal, dancers’ movements are acquired using UWB and two-dimensional (2D) LiDAR sensors. By inserting acquired movement data in the developed stage, a camera effect is produced using a virtual camera installed in the developed simulator. The camera effect comprises pan, tilt, and zoom, and a camera director creates lightning effects while evaluating the movements of virtual dancers on the virtual stage. In this study, four cameras were used, three of which were used for camera pan, tilt, and zoom control, and the fourth was used as a fixed camera for a full shot. Video shooting is performed according to the pan, tilt, and zoom values of the three cameras and switcher data. Only the video of dancers recorded during rehearsal and that produced by the lighting director via the existing broadcast filming process is overlapped in the developed simulator to assess lighting effects. The lighting director assesses the overlapping video and then corrects parts that require to be corrected or emphasized. The abovementioned method produced better lighting effects optimized for music and choreography compared to existing lighting effect production methods. Finally, the performance and lighting effects of the developed simulator and system were confirmed by shooting using K-pop using the pan, tilt, and zoom control plan, switcher sequence, and lighting effects of the selected camera.

Interest in video streaming has increased recently, as it constitutes most of the traffic on the Internet and cellular networks. These networks use different video streaming technologies. One of the most famous technologies is DASH (which stands for Dynamic Adaptive Steaming using HTTP). DASH adapts streaming parameters according to network conditions and uses the HTTP protocol to communicate between the user and the server. DASH faces many challenges that may lead to video interruptions and poor quality of user experiences (QoE) such as bad network conditions and buffering level control. In addition to the lack of studies, we cover security issues for these types of services. In this paper, we proposed an integrated framework that consists of four components: quality prediction model, precache model, light web application firewall, and a monitoring system. These four components improve QoE and precache and increase the level of security. The results of the quality prediction model are used to predict the quality of the next segments depending on the user’s network conditions and in the precache model to improve caching to reduce the load on the streaming system and rely more on cache servers. The proposed web application firewall is a light version used to defend against video streaming attacks and verify the existence of necessary HTTP headers. The quality predictor model with the generated dataset achieved 97% classification accuracy using DecisionTree, and this experiment proved the strong relationship between congestion periods and streaming quality, which is s the main key in QoE.

Ocean observation plays an important role in many areas, such as marine scientific research, environmental expedition, and resource exploitation. Traditional observation generally acquires periodical marine information and lacks real-time performances. To realize underwater observation of multi-information in real-time, an information acquisition system is designed in this study based on optical fiber communication. A multichannel interface between the diverse sensors and the slave computer is designed. It shields discrepancies of different sensor data and allows collecting information of various types, including conductivity, temperature, depth (CTD), attitudes, and images. At the same time, the full communication link is designed by constructing an optical fiber channel in collaboration with RS485 and USB. It provides a higher-rate link for real-time data transmission from underwater to overwater. Then, software procedures are, respectively, developed for the slave computer and the master computer based on the designed communication protocol. Additionally, a PC-based user interface is designed to realize data receipt, file saving, information display, and image restoration. Finally, an acquisition prototype is developed, and an underwater experiment is conducted to evaluate its performance. The results show that the underwater CTD, attitudes, and images can be collected and transmitted in real time. From its higher transmission rate and lower packet loss rate, the accuracy and the stability of the prototype are verified. The implementation of the acquisition prototype follows the trends of laying and networking submarine optical fiber cables. Therefore, its application can be expanded for ocean observation in information collecting, transmitting, and processing.

In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses on depth information that is lost owing to projection. This mathematical model is developed using a convolutional neural network with three modules for integration, reconstruction, and optimization, as well as two prior-free loss functions. The proposed RONN achieves competitive accuracy on several tested sequences and high visual quality of various real video sequences.

Based on the integration of the technology acceptance model and the information system success model, this study constructs the structural equation model of college students’ online ideological and political learning behavioral intention; then, a questionnaire survey is conducted among college students from 7 colleges of Chongqing and Hangzhou. The results show that there are group differences in system perception, technology acceptance perception, and behavioral intention among college students with different genders and grades; different dimensions of information system quality have different effects on behavioral intention; perceived ease of use is an important mediating variable, which has a direct positive impact on the use attitude; perceived usefulness and use attitude have positive direct effects on behavioral intention; and perceived usefulness has a higher predictive effect on behavioral intention. Therefore, we put forward four suggestions. First, in the future practice, we should pay attention to the group differences of college students and constantly improve the practice mechanism of network ideological and political education. Second, we propose to take the quality construction of information system as the goal and constantly consolidate the service system of network platform. Third, we believe that we can improve users’ attitude towards the use of the platform by improving their perception of ease of use. Finally, we also suggest that we should focus on improving users’ useful perception of the platform, so as to promote their use behavior.

In order to alleviate energy consumption of wireless sensor networks (WSNs), energy consumption optimization-based clustering routing (ECOR) is proposed in this paper. In ECOR, network is gridding by hexagon. And there is only a cluster head (CH) in each hexagon, which makes the distribution of CHs more even. Residual energy of nodes and distance from the centroid of hexagon are used to elect CHs. For any a CH, dynamic time slot allocation strategy is adopted to allocate time slot for its cluster members. According to status of cluster members, the duration of time slot is dynamically adjusted. In intercluster communication, the Dijkstra algorithm is used to construct the shortest path between CHs in order to shorten the distance of transmitting data. Simulation results show that the ECOR algorithm outperforms the improved-Low Energy Adaptive Clustering Hierarchy (I-LEACH) algorithm in terms of distribution of CHs and energy consumption.

This paper proposes an image quality evaluation (IQE) metric by considering gradient, visual saliency, and color information. Visual saliency and gradient information are two types of effective features for quality evaluation research. Different regions within an image are not uniformly important for IQE. Visual saliency can find the most attractive regions to the human visual system in a given image. These attractive image regions are more strongly correlated with image quality results. In addition, the degradation of gradient information is related to the structure distortion which is a very important factor for image quality. However, the two types of features cannot accurately evaluate the color distortion of images. In order to evaluate chromatic distortion, this paper proposes the color similarity which is measured in the YIQ color space. The computation of the proposed method begins with the similarity calculation of local gradient information, visual saliency, and color information. Then, the final quality score is obtained by the standard deviation on each similarity component. The experimental results on five benchmark databases (i.e., CSIQ, IVC, LIVE, TID2013, and TID2008) show that the proposed IQE method performs better than other methods in the correlation with subjective quality judgment.

Blockchain technology is widely used in the field of digital right protection technology. The traditional digital right protection scheme is not only inefficient and highly centralized but also has the risk of being modified. Due to its own characteristics, blockchain cannot completely store all the original files of digital resources. In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). This algorithm can recognize facial expressions in the original files of digital resources (for short video of face class). The algorithm extracts facial expression features accurately and makes these features form log files that can represent the original files of digital resources. Then, the paper proposes a short video copyright storage algorithm based on blockchain and facial expression recognition and stores the log file into the blockchain. The above methods not only improve the efficiency of short video copyright storage, reduce the degree of storage centralization, and eliminate the risk that copyright is easy to be modified. Moreover, the computing operation of deep learning technology on short video not only ensures the privacy of storage certificate information but also ensures the possibility of blockchain storage of video information. Experiments show that the algorithm proposed in this paper is more efficient than the traditional copyright storage method. Moreover, the algorithm proposed in this paper can provide technical support to the media resource management department.

For multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the key factor that causes the system sum-rate to change is its interference plus noise, corresponding to the number of base station (BS) antennas and signal-to-noise ratio (SNR) variation causing the system sum-rate changes, which in turn affects the user’s communication quality. Based on this, this paper proposes an improved low-complexity hybrid beamforming grouping sum-rate maximization (HBG-SRM) algorithm to achieve system sum-rate maximization under the premise that both BS and user side use hybrid beamforming architecture and the channel state information (CSI) of the downlink channel is perfect. The algorithm first predefines a relevant threshold value, which is used to group multiusers; then, the user uses the maximum likelihood (ML) criterion to identify the optimal beam and estimate its beamforming gain within each group, and finally, the user compares all the candidate optimal beam gains between each group to confirm the optimal beamforming vector. The simulation results also verify the superiority of the proposed algorithm’s sum-rate over other algorithms.

The pervasive acceptability of a revolution from monodirectional push-based media broadcasting to a bidirectional interactive pull-based internet protocol television (IPTV) has spotted significant development in recent years. The pervasive acceptability is because of the mammoth number of exhilarating television (TV) channels that IPTV offers. However, the channel switching feature of a TV system requires additional development despite the increased implementation of IPTV systems worldwide. Subscribers of IPTV services must be able to swiftly explore live TV stations and video contents of interest seamlessly, but zapping delay is a deterrent that occurs during a channel change that causes a significant glitch in IPTV systems. Many of the literature approaches such as channel prediction based on behavior analysis have shown flaws in resolving zapping delay. The approach of this study uses adaptive channel switching with a personalized electronic program guide to resolving zapping delay. The resolution saves the subscribers the time of channel navigation by eliminating the need to search for channels they want to view.

Future Video Coding (FVC) is a modern standard in the field of video coding that offers much higher compression efficiency than the HEVC standard. FVC was developed by the Joint Video Exploration Team (JVET), formed through collaboration between the ISO/IEC MPEG and ITU-T VCEG. New tools emerging with the FVC bring in super resolution implementation schemes that are being recommended for Ultra-High-Definition (UHD) video coding in both SDR and HDR images. However, a new flexible block structure is adopted in the FVC standard, which is named quadtree plus binary tree (QTBT) in order to enhance compression efficiency. In this paper, we provide a fast FVC algorithm to achieve better performance and to reduce encoding complexity. First, we evaluate the FVC profiles under All Intra, Low-Delay P, and Random Access to determine which coding components consume the most time. Second, a fast FVC mode decision is proposed to reduce encoding computational complexity. Then, a comparison between three configurations, namely, Random Access, Low-Delay B, and Low-Delay P, is proposed, in terms of Bitrate, PSNR, and encoding time. Compared to previous works, the experimental results prove that the time saving reaches 13% with a decrease in the Bitrate of about 0.6% and in the PSNR of 0.01 to 0.2 dB.