2014/04/15 by Yehuda Dar, Alfred M. Bruckstein, Alfred M. Bruckstein⋆ +2
Computer Science · #Advanced Data Compression Techniques #Advanced Vision and Imaging #FOS: Computer and information sciences #Multimedia (cs.MM) #Video Coding and Compression Technologies #cs.MM
paper · pdf · doi:10.48550/arxiv.1404.4026
openalex publication_date 2014/04/15 · arxiv created 2015/04/24 · arxiv updated 2015/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Good quality video coding for low bit-rate applications is important for transmission over narrow-bandwidth channels and for storage with limited memory capacity. In this work, we develop a previous analysis for image compression at low bit-rates to adapt it to video signals. Improving compression using down-scaling in the spatial and temporal dimensions is examined. We show, both theoretically and experimentally, that at low bit-rates, we benefit from applying spatio-temporal scaling. The proposed method includes down-scaling before the compression and a corresponding up-scaling afterwards, while the codec itself is left unmodified. We propose analytic models for low bit-rate compression and spatio-temporal scaling operations. Specifically, we use theoretic models of motion-compensated prediction of available and absent frames as in coding and frame-rate up-conversion (FRUC) applications, respectively. The proposed models are designed for multi-resolution analysis. In addition, we formulate a bit-allocation procedure and propose a method for estimating good down-scaling factors of a given video based on its second-order statistics and the given bit-budget. We validate our model with experimental results of H.264 compression.