The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric.

翻译：人工智能（AI）技术的快速发展使得利用神经网络进行视频处理、编码和传输的标准化工作成为优先方向。为应对这一优先领域，移动图像、音频与数据编码人工智能（MPAI）组织正在开发一套名为MPAI-EEV的标准系列，用于"端到端优化的神经视频编码"。该基于AI的视频标准项目旨在通过利用数据训练的神经编码技术，压缩表示高保真视频数据所需的比特数。该方法不受传统混合框架中数据编码方式的约束。本文概述了该领域近期的标准化工作及正在进行的努力，并重点介绍了EEV的关键技术和设计理念。同时，本文对参考模型的编码效率等若干核心工作进行了比较与报告。此外，还讨论了当前已规划、正在开发或处于探索阶段的新兴活动，例如基于学习的无人机（UAV）视频编码。以无人机视频信号为重点，本文阐述了这些前期工作的当前状态，并指明了开发时间线，总结了主要技术细节，提供了进一步参考的指引。探索实验表明，在感知评价指标方面，EEV模型优于当前最先进的视频编码标准H.266/VVC。