Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression
원문 초록
With the increasing demand for video content at higher resolutions, it is evermore critical to find ways to limit the complexity of video encoding tasks in order to reduce costs, power consumption and environmental impact of video services. In the last few years, algorithms based on Neural Networks (NN) have been shown to benefit many conventional video coding modules. But while such techniques can considerably improve the compression efficiency, they usually are very computationally intensive. It is highly beneficial to simplify models learnt by NN so that meaningful insights can be exploited with the goal of deriving less complex solutions. This paper presents two ways to derive simplified intra-prediction from learnt models, and shows that these streamlined techniques can lead to efficient compression solutions.
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.