2024/10/21 by Anna Meyer, Meyer, Anna, André Kaup +1
Computer Science · #Advanced Data Compression Techniques #FOS: Electrical engineering #Image Enhancement Techniques #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.15873
openalex publication_date 2024/10/21 · openalex created_date 2024/11/07 · openalex updated_date 2026/07/28
Learned wavelet video coders provide an explainable framework by performing discrete wavelet transforms in temporal, horizontal, and vertical dimensions. With a temporal transform based on motion-compensated temporal filtering (MCTF), spatial and temporal scalability is obtained. In this paper, we introduce variable rate support and a mechanism for quality adaption to different temporal layers for a higher coding efficiency. Moreover, we propose a multi-stage training strategy that allows training with multiple temporal layers. Our experiments demonstrate Bjøntegaard Delta bitrate savings of at least -32% compared to a learned MCTF model without these extensions. Training and inference code is available at: https://github.com/FAU-LMS/Learned-pMCTF.