Flexible-Rate Learned Hierarchical Bi-Directional Video Compression With Motion Refinement and Frame-Level Bit Allocation
원문 초록
This paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bi-directional video compression to further advance the state-of-the-art in learned video compression. As an improvement, we combine motion estimation and prediction modules and compress refined residual motion vectors for improved rate-distortion performance. As novel addition, we adapted the gain unit proposed for image compression to flexible-rate video compression in two ways: first, the gain unit enables a single encoder model to operate at multiple rate-distortion operating points; second, we exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate that we obtain state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model
Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens
High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
Generative Latent Coding for Ultra-Low Bitrate Image and Video Compression
TVC: Tokenized Video Compression with Ultra-Low Bitrate
L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression
토론
이 논문과 연결된 포럼 글이 아직 없습니다.