SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking

Zilin Fang, Andrey Ignatov, Eduard Zamfir, Radu Timofte; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 20532-20542

Smartphone photography is becoming increasingly popular, but fitting high-performing camera systems within the given space limitations remains a challenge for manufacturers. As a result, powerful mobile camera systems are in high demand. Despite recent progress in computer vision, camera system quality assessment remains a tedious and manual process. In this paper, we present the Smartphone Camera Quality Assessment Dataset (SQAD), which includes natural images captured by 29 devices. SQAD defines camera system quality based on six widely accepted criteria: resolution, color accuracy, noise level, dynamic range, Point Spread Function, and aliasing. Built on thorough examinations in a controlled laboratory environment, SQAD provides objective metrics for quality assessment, overcoming previous subjective opinion scores. Moreover, we introduce the task of automatic camera quality assessment and train deep learning-based models on the collected data to perform a precise quality prediction for arbitrary photos. The dataset, codes and pre-trained models are released at https://github.com/aiff22/SQAD.