pith. sign in

arxiv: 2211.05910 · v1 · pith:GVBASO37new · submitted 2022-11-07 · 📡 eess.IV · cs.CV

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

Andrey Ignatov , Radu Timofte , Maurizio Denna , Abdel Younes , Ganzorig Gankhuyag , Jingang Huh , Myeong Kyun Kim , Kihwan Yoon
show 88 more authors
Hyeon-Cheol Moon Seungho Lee Yoonsik Choe Jinwoo Jeong Sungjei Kim Maciej Smyl Tomasz Latkowski Pawel Kubik Michal Sokolski Yujie Ma Jiahao Chao Zhou Zhou Hongfan Gao Zhengfeng Yang Zhenbing Zeng Zhengyang Zhuge Chenghua Li Dan Zhu Mengdi Sun Ran Duan Yan Gao Lingshun Kong Long Sun Xiang Li Xingdong Zhang Jiawei Zhang Yaqi Wu Jinshan Pan Gaocheng Yu Jin Zhang Feng Zhang Zhe Ma Hongbin Wang Hojin Cho Steve Kim Huaen Li Yanbo Ma Ziwei Luo Youwei Li Lei Yu Zhihong Wen Qi Wu Haoqiang Fan Shuaicheng Liu Lize Zhang Zhikai Zong Jeremy Kwon Junxi Zhang Mengyuan Li Nianxiang Fu Guanchen Ding Han Zhu Zhenzhong Chen Gen Li Yuanfan Zhang Lei Sun Dafeng Zhang Neo Yang Fitz Liu Jerry Zhao Mustafa Ayazoglu Bahri Batuhan Bilecen Shota Hirose Kasidis Arunruangsirilert Luo Ao Ho Chun Leung Andrew Wei Jie Liu Qiang Liu Dahai Yu Ao Li Lei Luo Ce Zhu Seongmin Hong Dongwon Park Joonhee Lee Byeong Hyun Lee Seunggyu Lee Se Young Chun Ruiyuan He Xuhao Jiang Haihang Ruan Xinjian Zhang Jing Liu Garas Gendy Nabil Sabor Jingchao Hou Guanghui He
This is my paper
classification 📡 eess.IV cs.CV
keywords mobileimagechallengemodelsnpusquantizedsuper-resolutioncompatible
0
0 comments X
read the original abstract

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training

    cs.CV 2026-04 unverdicted novelty 4.0

    A quantized INT8 SISR model with extract-refine-upsample design and teacher-guided three-stage training achieves 29.79 dB PSNR and 0.8634 SSIM on the MAI 2026 Quantized 4K challenge under mobile INT8 constraints.