Communication Dans Un Congrès Année : 2024

Video Encoding Enhancement via Content-Aware Spatial and Temporal Super-Resolution

Résumé

Content-aware deep neural networks (DNNs) are trending in Internet video delivery. They enhance quality within bandwidth limits by transmitting videos as low-resolution (LR) bitstreams with overfitted super-resolution (SR) model streams to reconstruct high-resolution (HR) video on the decoder end. However, these methods underutilize spatial and temporal redundancy, compromising compression efficiency. In response, our proposed video compression framework introduces spatial-temporal video super-resolution (STVSR), which encodes videos into low spatial-temporal resolution (LSTR) content and a model stream, leveraging the combined spatial and temporal reconstruction capabilities of DNNs. Compared to the state-of-the-art approaches that consider only spatial SR, our approach achieves bitrate savings of 18.71 % and 17.04 % while maintaining the same PSNR and VMAF, respectively.

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Dates et versions

hal-04817180 , version 1 (03-12-2024)

Identifiants

Citer

Yiying Wei, Hadi Amirpour, Ahmed Telili, Wassim Hamidouche, Guo Lu, et al.. Video Encoding Enhancement via Content-Aware Spatial and Temporal Super-Resolution. 2024 32nd European Signal Processing Conference (EUSIPCO), Aug 2024, Lyon, France. pp.681-685, ⟨10.23919/eusipco63174.2024.10714942⟩. ⟨hal-04817180⟩
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