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Data Overfitting for On-device Super-Resolution with Dynamic Algorithm and Compiler Co-design

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15125))

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Abstract

Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take advantage of DNN’s overfitting properties to perform video resolution upscaling. By splitting videos into chunks and applying a super-resolution (SR) model to overfit each chunk, this scheme of SR models plus video chunks is able to replace traditional video transmission to enhance video quality and transmission efficiency. However, many models and chunks are needed to guarantee high performance, which leads to tremendous overhead on model switching and memory footprints at the user end. To resolve such problems, we propose a Dynamic Deep neural network assisted by a Content-Aware data processing pipeline to reduce the model number down to one (Dy-DCA), which helps promote performance while conserving computational resources. Additionally, to achieve real acceleration on the user end, we designed a framework that optimizes dynamic features (e.g., dynamic shapes, sizes, and control flow) in Dy-DCA to enable a series of compilation optimizations, including fused code generation, static execution planning, etc. By employing such techniques, our method achieves better PSNR and real-time performance (33 FPS) on an off-the-shelf mobile phone. Meanwhile, assisted by our compilation optimization, we achieve a 1.7\(\times \) speedup while saving up to 1.61\(\times \) memory consumption. Code available in https://github.com/coulsonlee/Dy-DCA-ECCV2024.

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Acknowledgments

This work is partly supported by the National Science Foundation CCF-2312616, CCF-2427875, CNS-2232048 and National Aeronautics and Space Administration (NASA) 80NSSC23K1393. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF and NASA.

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Authors and Affiliations

  1. Clemson University, Clemson, USA

    Gen Li, Jie Ji, Minghai Qin, Fatemeh Afghah & Xiaolong Ma

  2. University of Georgia, Athens, USA

    Zhihao Shu, Minghai Qin & Wei Niu

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  1. Gen Li
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  2. Zhihao Shu
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  3. Jie Ji
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  4. Minghai Qin
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  5. Fatemeh Afghah
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  6. Wei Niu
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  7. Xiaolong Ma
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Correspondence to Gen Li .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Li, G. et al. (2025). Data Overfitting for On-device Super-Resolution with Dynamic Algorithm and Compiler Co-design. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15125. Springer, Cham. https://doi.org/10.1007/978-3-031-72855-6_21

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