Department of Directing, Qingdao Film Academy, Qingdao City 266000, China.
Department of Media, Arts and Humanities, University of Sussex, Brighton BN1 9RH, UK.
Comput Intell Neurosci. 2022 May 20;2022:8918073. doi: 10.1155/2022/8918073. eCollection 2022.
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.
如今，新媒体技术已经广泛渗透到影视等艺术形式中，改变了新媒体环境下的视觉表现方式。为了更好地解决沉浸感弱、互动性差、模拟程度低的问题，本工作采用深度学习技术和虚拟现实（VR）技术对电影播放效果进行优化。首先，采用优化的极值中值滤波器算法对单视频图像的“毛刺”现象和低压缩比进行优化。其次，利用深度学习技术中的生成式对抗网络（GAN）对单视频图像的数据进行增强。最后，采用决策树算法和层次聚类算法对 VR 图像进行色彩增强。实验结果表明，该系统优化后的单帧图像对比度为 4.21，熵为 8.66，噪声比为 145.1，表明该方法可以有效调整对比度参数，防止细节丢失，降低刺眼强度。所提出的 GAN 生成的特定类型图像的质量和多样性得到了提高，与具有监督的当前主流 GAN 方法相比，与人类的主观评价结果一致。与自注意力生成对抗网络相比，Frechet Inception Distance 值也有显著提高。这表明所提出的方法生成的样本具有精确的细节和丰富的纹理特征。该方案为优化 VR 电影的互动性、沉浸感和模拟度提供了参考。
