arXiv:2611.05024v1 Announce Type: cross
Abstract: On-device RGB photo enhancement is a popular task that is needed for quality improvement of photos captured with mobile devices as well as for retouching of images acquired through other sources. This creates a need for efficient deep learning models for this problem that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose participants to design fast solutions that can perform comprehensive enhancement of RGB photos while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale DPED dataset consisting of RGB image pairs captured with an old iPhone 3GS smartphone and a professional Canon 70D DSLR camera. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to enhance Full HD resolution RGB images on both platforms under 300 milliseconds. A comprehensive description of the models developed in the challenge is provided in this paper.
arxiv
Mobile AI 2026 Challenge Report: Efficient RGB Photo Enhancement on Smartphone GPUs
This paper presents the Mobile AI 2026 challenge on on-device RGB photo enhancement, targeting efficient deep learning models for smartphone GPUs. Participants used the DPED dataset and optimized for Qualcomm and Arm chipsets, with top solutions enhancing Full HD images in under 300 ms. The report details the models and results, highlighting practical deployment on constrained hardware.
Provides concrete, recent results on efficient mobile AI photo enhancement, valuable for practitioners and researchers in on-device deep learning.
Comments & Questions (0)
No comments yet
Be the first to comment!