Video Compression through Image Interpolation
원문 초록
An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives through them, all of which are powered by strong video compression. Traditional video compression is laboriously hand designed and hand optimized. This paper presents an alternative in an end-to-end deep learning codec. Our codec builds on one simple idea: Video compression is repeated image interpolation. It thus benefits from recent advances in deep image interpolation and generation. Our deep video codec outperforms today's prevailing codecs, such as H.261, MPEG-4 Part 2, and performs on par with H.264.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Video Compression for Spatiotemporal Earth System Data
Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model
BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression
FG-DFPN: Flow Guided Deformable Frame Prediction Network
Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
토론
이 논문과 연결된 포럼 글이 아직 없습니다.