Neural Video Compression using Spatio-Temporal Priors
The pursuit of higher compression efficiency continuously drives the advances of video coding technologies. Fundamentally, we wish to find better "predictions" or "priors" that are reconstructed previously to remove the signal dependency efficiently and to accurately model the signal distribution for entropy coding. In this work, we propose a neural video compression framework, leveraging the spatial and temporal priors, independently and jointly to exploit the correlations in intra texture, optical flow based temporal motion and residuals. Spatial priors are generated using downscaled low-resolution features, while temporal priors (from previous reference frames and residuals) are captured using a convolutional neural network based long-short term memory (ConvLSTM) structure in a temporal recurrent fashion. All of these parts are connected and trained jointly towards the optimal rate-distortion performance. Compared with the High-Efficiency Video Coding (HEVC) Main Profile (MP), our method has demonstrated averaged 38% Bjontegaard-Delta Rate (BD-Rate) improvement using standard common test sequences, where the distortion is multi-scale structural similarity (MS-SSIM).
Code (0)
등록된 구현이 없습니다.
Tasks
MS-SSIMOptical Flow EstimationSSIMVideo CompressionSimilar Papers 제목 키워드 기반
Generative Neural Video Compression via Video Diffusion Prior
We present GNVC-VD, the first DiT-based generative neural video compression framework built upon an advanced video generation foundation model, where spatio-temporal latent compression and sequence-level generative refin…
Video GenerationLow-Bitrate Video Compression through Semantic-Conditioned Diffusion
Traditional video codecs optimized for pixel fidelity collapse at ultra-low bitrates and produce severe artifacts. This failure arises from a fundamental misalignment between pixel accuracy and human perception. We propo…
multimodal generationSTeP: A General and Scalable Framework for Solving Video Inverse Problems with Spatiotemporal Diffusion Priors
We study how to solve general Bayesian inverse problems involving videos using diffusion model priors. While it is desirable to use a video diffusion prior to effectively capture complex temporal relationships, due to th…
Unified Spatiotemporal Token Compression for Video-LLMs at Ultra-Low Retention
Video large language models (Video-LLMs) face high computational costs due to large volumes of visual tokens. Existing token compression methods typically adopt a two-stage spatiotemporal compression strategy, relying on…
Semantic SimilarityQuestion AnsweringDVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration
Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coherence. Recent diffusion-based methods have brought strong generative …