Abstract
With the increasing development of multimedia teaching, the combination of virtual reality (VR) and video image control has very attractive development prospects in ideological and political teaching, for example, the use of virtual technology in games and so on. However, most virtual reality environments are currently built, and the functional development of artificial intelligence multimedia teaching systems is not comprehensive. An artificial intelligence VR video image control system is constructed for the multimedia teaching system. This article analyzes the development of artificial intelligence multimedia teaching systems and compares the detection performance and efficiency of traditional methods and artificial intelligence multimedia VR ideological and political teaching. Research shows that, in the use of VR to control the images of ideological and political teaching, the average accuracy of these ten video images is 75.68%. This shows that the video image classification and detection algorithm model based on artificial intelligence in this paper can extract deeper and more abstract features to classify the target. The artificial intelligence VR video image control algorithm constructed in this paper can reduce the maximum failure rate by 49.16%, 61.02%, and 66.94%, respectively. Compared with the traditional algorithm, the artificial intelligence VR video image control algorithm constructed in this paper can reduce the storage access delay time of 10 different video images by an average of 15.93%, can obtain about 9.37% performance optimization, and can reduce the video image control time by 7.28% and 10.63%, respectively. For pictures, the artificial intelligence VR video image control system in this article can increase the performance by up to 28.49%.
Highlights
At present, related researches on cognitive education focus on plane and three-dimensional graphics, and there are little researches on graphics processing in virtual environments
The conclusions drawn in the plane and three-dimensional learning environment may not be applicable to the virtual reality environment, which is the first influencing factor concerned in cognitive education
Based on the detailed analysis of virtual reality (VR) technology, this paper constructs an embedded VR video image control system for multimedia teaching systems and uses the systemic teaching design ideas to analyze the development of multimedia teaching. is paper introduces the construction of video image datasets, analyzes the influence of network parameters on the final detection accuracy, and compares the detection performance and efficiency of traditional methods and the design method of this paper. e embedded VR video image control algorithm constructed in this paper can reduce the maximum failure rate by 49.16%, 61.02%, and 66.94%, respectively
Summary
At present, related researches on cognitive education focus on plane and three-dimensional graphics, and there are little researches on graphics processing in virtual environments. In the virtual reality learning environment, the sensory experience of the learner is richer and more immersive than the plane and three-dimensional space, and the cognitive load may change. The conclusions drawn in the plane and three-dimensional learning environment may not be applicable to the virtual reality environment, which is the first influencing factor concerned in cognitive education. Stepan proposed that cognitive education is caused by learning tasks, learners, and the interaction between the two [2]. Guo believes that cognitive education has many influencing factors, such as the number of items, presentation time, and text clues, which will have an impact
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