Multimedia Image Encryption Analysis Based on High-Dimensional Chaos Algorithm

With the development of network and multimedia technology, multimedia communication has attracted the attention of researchers. Image encryption has become an urgent need for secure multimedia communication. Compared with the traditional encryption system, encryption algorithms based on chaos are easier to implement, which makes them more suitable for large-scale data encryption. The calculation method of image encryption proposed in this paper is a combination of high-dimensional chaotic systems. This algorithm is mainly used for graph mapping and used the Lorenz system to expand and replace them one by one. Studies have shown that this calculation method causes mixed pixel values, good diffusion performance, and strong key performance with strong resistance. The pixel of the encrypted picture is distributed relatively random, and the characteristics of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance.

A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems

The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods.

The Construction of English Smart Classroom and the Innovation of Teaching Mode under the Background of Internet of Things Multimedia Communication

There are some problems in English smart classroom, such as slow response speed of related platforms and room for improvement of teaching effect. Therefore, it is necessary to construct English smart classroom and innovate teaching mode under the background of Internet of Things (IoT) multimedia communication. The teaching objectives are divided into classroom teaching objectives, curriculum objectives, and education and training objectives, so as to design the technical support for English smart classroom, that is, to build a smart classroom integrating cloud application platform (including students’ learning cloud and teachers’ teaching cloud), teaching platform, resource platform, learning space, and interactive classroom by using Internet of Things multimedia communication technology, and build an innovative teaching mode of English wisdom classroom. The fuzzy hierarchical evaluation model is used to design the fuzzy hierarchical evaluation algorithm. Through the fuzzy hierarchical evaluation algorithm, the teaching effect of smart classroom is evaluated, and the final evaluation result is obtained. The results show that the average platform login time of the proposed method is always below 0.3 s, the average score of the summative evaluation is high, the evaluation time is always below 0.4 s, the average communication delay is 0.54 s, and the user satisfaction is always above 9.2. The practical application effect is better.

Assessment of Machine Learning-Based Audiovisual Quality Predictors

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

Application of Multimedia Communication Technology in Network Teaching

<p>With the development of multimedia, communication technology and information processing technology, multimedia communication technology has been widely used, and many universities have opened multimedia communication courses for information and communication majors. This article analyzes the application of multimedia communication technology in network teaching, and hopes to be helpful to related researchers.</p>