July 14, 2022, 1:12 a.m. | Jiahao Li, Bin Li, Yan Lu

cs.CV updates on arXiv.org arxiv.org

For neural video codec, it is critical, yet challenging, to design an
efficient entropy model which can accurately predict the probability
distribution of the quantized latent representation. However, most existing
video codecs directly use the ready-made entropy model from image codec to
encode the residual or motion, and do not fully leverage the spatial-temporal
characteristics in video. To this end, this paper proposes a powerful entropy
model which efficiently captures both spatial and temporal dependencies. In
particular, we introduce the …

arxiv compression entropy hybrid modelling temporal video video compression

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Risk Management - Machine Learning and Model Delivery Services, Product Associate - Senior Associate-

@ JPMorgan Chase & Co. | Wilmington, DE, United States

Senior ML Engineer (Speech/ASR)

@ ObserveAI | Bengaluru