A detailed caption for this image could be: A poster presentation titled "Neural Video Compression with Feature Modulation," authored by Jiahao Bu, Li Li, Bin Li, and Yan Lu from Microsoft Research Asia, displayed at the CVPR conference in Seattle, WA, June 17-21, 2024. The poster is organized into three main sections: Introduction, Methods, and Results. **Introduction:** Features a graph comparing different video codecs, highlighting the superior performance of the Neural Video Codec (NVC) over ECM in long prediction chains. Key points include that NVC supports a large quality range for both YUV and RGB content, achieves 29.7% bitrate saving, and incurs a 16% MAC reduction over previous state-of-the-art NVC. **Methods:** The core framework of the conditional coding-based structure is illustrated, showing the process flow from the input frame to the frame coding function and temporal feature modulation stages. Diagrams detail the components and operational steps in the video compression method. **Results:** Includes rate control graphics and a table comparing BD-rate percentages across various codecs, where lower values indicate better performance. Visual examples of video quality under different codecs are provided, comparing the original to ECM-5.0, DCVC, and DCVC-FM codecs. The GitHub link for the project is also provided: https://github.com/microsoft/DCVC. The poster is neatly structured and visually communicates the advancements in neural video compression achieved by the research team. Text transcribed from the image: 11.3 dB PSNR (dB) 45 43 Introduction DCVC-FM 41 DCVC-DC 3.6 dB 37 Bate Decrease 20% 40% DCVC-OC HM-16.25 0% 0.004 80% 84.4% 42.8 % ECM-11.0 VIM 170 HM-16.25 30.2% 17.2% VTM-17.0 ECM-11.0 DCVC-FM RPP 0.012 0.016 0.020 The first neural video codec (NVC) that surpasses ECM under long prediction chain Support a large quality range for both YUV and RGB contents in single model 29.7% bitrate saving and 16% MAC reduction over previous SOTA NVC https://github.com/microsoft/DCVC Neural Video Compression with Feature Modulation Jiahao Li Bin Li Yan Lu Microsoft Research Asia X-2 Methods t-1 Ct-2 F₂ fframe ITcontext fframe frcontext fframe + Feature Propagation 01-1 De 9t-2 fmotion 9-1 fmotion Qt Xt-2 xt-1 Our conditional coding-based framework we xt Enigh Feature y ヴィー Extractor AD Rt sec Daigh Diow Fr wder Ct Cr Frame coding function 91-1 Entropy 2 Model C Xt frontext F-1- Feature Extractor Motion Alignment Context Rebesh Control P Temporal feature modulation 5+ Results CVPR SEATTLE, WA JUNE 17-21, 2024 3000 High bitrate scenario 600 Target bitrate Actual bitrate Low bitrate scenario 2500 500- 1500 1000 100 200 300 Frame Index 300 Target bitrate Actual bitrate 200 200 Frame index Rate control examples UVG MCL-JCV VTM-17.0 0.0 0.0 HM-16.25 40.1 48.61 HEVC B HEVC C HEVC D HEVC E Average 0.0 0.0 0.0 0.0 0.0 47.6 41.0 34.5 42.8 42.4 ECM-5.0 -14.9 -17.0 -17.3 -16.6 -16.1 -14.1 -16.0 ECM-11.0 -20.0 -22.1 -22.2 -21.2 -20.4 -17.2 -20.5 DCVC-DC 5.7 -5.0 12.2 -4.2 -16.5 84.4 12.8 DCVC-FM -20.7 -10.3 -18.2 -32.2 -41.2 -30.2 -25.5 BD-Rate (%) comparison, and the lower, the better Original ECM-5.0 DCVC-DC DCVC-FM BPP/PSNR 0.0184/32.74 0.0184/31.96 0.0178/33.36 BAY QUARTER BAY QUARTER BAY QUARTER BAY QUARTER BPP/PSNR 0.0183/35.22 0.0180/36.37 0.0180/34.93 Visual comparison