Film Effect Optimization by Deep Learning and Virtual Reality Technology in New Media Environment

Abstract

Today, new media technology has widely penetrated art forms such as film and television, which has changed the way of visual expression in the new media environment. To better solve the problems of weak immersion, poor interaction, and low degree of simulation, the present work uses deep learning technology and virtual reality (VR) technology to optimize the film playing effect. Firstly, the optimized extremum median filter algorithm is used to optimize the “burr” phenomenon and a low compression ratio of the single video image. Secondly, the Generative Adversarial Network (GAN) in deep learning technology is used to enhance the data of the single video image. Finally, the decision tree algorithm and hierarchical clustering algorithm are used for the color enhancement of VR images. The experimental results show that the contrast of a single-frame image optimized by this system is 4.21, the entropy is 8.66, and the noise ratio is 145.1, which shows that this method can effectively adjust the contrast parameters to prevent the loss of details and reduce the dazzling intensity. The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings. The Frechet Inception Distance value is also significantly improved compared with Self-Attention Generative Adversarial Network. It shows that the sample generated by the proposed method has precise details and rich texture features. The proposed scheme provides a reference for optimizing the interactivity, immersion, and simulation of VR film.

Multimedia VR Image Improvement and Simulation Analysis Based on Visual VR Restructuring Algorithm

“…In this study, the field-programmable gate array (FPGA), digital signal processing (DSP), and simulation system-based video image optimization [27] are compared with image processing techniques. The simulation results are shown in Figure 6.…”

Animation Vr Scene Stitching Modeling Based on Genetic Algorithm

“…(𝑚, 𝑛) = { 𝑁 𝑗 (𝑚 , ), 𝑁 𝑗 (𝑚, 𝑛) ≥ 𝑀 𝑖 (𝑚, 𝑛) 𝑀 𝑗 (𝑚, 𝑛), 𝑁 𝑖 (𝑚, 𝑛) < 𝑀 𝑗 (𝑚, 𝑛) (9) As stated in equation 10, the final splicing coefficient is calculated using the scene entropy function and evaluation function.…”

“…Take the animation(s) from the final population that has the greatest fitness score once the algorithm has finished running. These animations show the created or optimized answers to animation issues [9]. A search and optimization method called a GA draws inspiration from natural selection and evolution.…”

Realistic image generation in virtual reality using deep learning techniques

“…Traditional image generation methods are difficult to meet these requirements because the realism of virtual environments and characters requires higher levels of detail and realism. Therefore, the motivation of this study is to use DL technology, especially CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network), to improve the quality and realism of VR image generation 2 . This article first introduces the outstanding achievements of DL technology in the field of computer vision, including breakthroughs in tasks such as image classification, object detection, and semantic segmentation.…”