Advances in Neural Video Compression: A Review and Benchmarking

While conventional video coding standards remain predominant in real-world applications, neural video compression has emerged over the past decade as an active research area, offering alternative solutions with potentially significant coding gains through end-to-end optimization. Owing to the rapid pace of recent progress, existing reviews of neural video coding quickly become outdated and often lack a systematic taxonomy and meaningful benchmarking. To address this gap, we provide a comprehensive review of two major classes of neural video codecs - scene-agnostic and scene-adaptive - with a focus on their design characteristics and limitations. More importantly, we benchmark representative state-of-the-art methods from each category under common test conditions recommended by video coding standardization bodies. This provides, to the best of our knowledge, the first first large-scale unified comparison between conventional and neural video codecs under controlled settings. Our results show that neural codecs can already achieve competitive, and in some cases superior, performance relative to VTM and AVM, although they still fall short of ECM in overall coding efficiency under both Low Delay and Random Access configurations. To facilitate future algorithm benchmarking, we will release the full implementations and results at https://nvc-review-2025.github.io, thereby providing a useful resource for the video compression research community.