arXiv:2106.08124
Quality assessment methods for perceptual video compression
원문 초록
This paper describes a quality assessment model for perceptual video compression applications (PVM), which stimulates visual masking and distortion-artefact perception using an adaptive combination of noticeable distortions and blurring artefacts. The method shows significant improvement over existing quality metrics based on the VQEG database, and provides compatibility with in-loop rate-quality optimisation for next generation video codecs due to its latency and complexity attributes. Performance comparison are validated against a range of different distortion types.
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.