基于深度学习的视频压缩算法优化及其性能评估  

Optimization and Performance Evaluation of Video Compression Algorithm Based on Deep Learning

作　　者：陈丰 赵雄英 杨金贵 杨盼盼 杨燕 CHEN Feng;ZHAO Xiongying;YANG Jingui;YANG Panpan;YANG Yan

机构地区：[1]云南滇能智慧能源有限公司,云南昆明650228

出　　处：《今日自动化》2025年第2期7-9,共3页Automation Today

摘　　要：文章提出了一种基于深度学习的新型视频压缩算法,旨在优化传统视频压缩技术的性能缺陷。通过结合卷积神经网络和递归神经网络,本算法显著提高了压缩效率和视频质量。实验结果显示,相比于传统的H.264和H.265压缩标准,新算法在保持相同视频质量的情况下,压缩率提高了约20%。此外,文章还系统评估了新算法在不同类型视频数据上的性能,验证了其广泛的适用性和优越的性能。This article proposes a novel deep learning based video compression algorithm aimed at optimizing the performance shortcomings of traditional video compression techniques.By combining Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN),this algorithm significantly improves compression efficiency and video quality.The experimental results show that compared to the traditional H.264 and H.265 compression standards,the new algorithm improves the compression rate by about 20%while maintaining the same video quality.In addition,this article systematically evaluates the performance of the algorithm on different types of video data,verifying its wide applicability and superior performance.

关 键 词：深度学习 视频压缩 性能评估 卷积神经网络 递归神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]