Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
원문 초록
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions have been proposed for this task, the majority of them are too computationally expensive to run on portable devices with limited hardware resources. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a real-time performance on mobile GPUs. The participants were provided with the REDS dataset and trained their models to do an efficient 4X video upscaling. The runtime of all models was evaluated on the OPPO Find X2 smartphone with the Snapdragon 865 SoC capable of accelerating floating-point networks on its Adreno GPU. The proposed solutions are fully compatible with any mobile GPU and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.
함께 살펴볼 논문
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HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
Compressed Video Super-Resolution based on Hierarchical Encoding
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
토론
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