Perceptual Quality Assessment of Smartphone Photography

By Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, Zhou Wang. Computer Vision and Pattern Recognition, 2020

470 citations recorded by the source corpus.

As smartphones become people’s primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market. We conduct so far the most comprehensive study of perceptual quality assessment of smartphone photography. We introduce the Smartphone Photography Attribute and Quality (SPAQ) database, consisting of 11,125 pictures taken by 66 smartphones, where each image is attached with so far the richest annotations. Specifically, we collect a series of human opinions for each image, including image quality, image attributes (brightness, colorfulness, contrast, noisiness, and sharpness), and scene category labels (animal, cityscape, human, indoor scene, landscape, night scene, plant, still life, and others) in a well-controlled laboratory environment. The exchangeable image file format (EXIF) data for all images are also recorded to aid deeper analysis. We also make the first attempts using the database to train blind image quality assessment (BIQA) models constructed by baseline and multi-task deep neural networks. The results provide useful insights on how EXIF data, image attributes and high-level semantics interact with image quality, how next-generation BIQA models can be designed, and how better computational photography systems can be optimized on mobile devices. The database along with the proposed BIQA models are available at https://github.com/h4nwei/SPAQ.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/4de98c68c2d6a9f2d9ee2af1ed18e54e0435f952

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Citations in the corpus

Exploring CLIP for Assessing the Look and Feel of Images (2022, methodology): “Figure 3: Best-scored and worst-scored images labeled by CLIP-IQA in SPAQ (Fang et al. 2020).”
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild (2020, methodology): “To build the playground for gMAD, we combine all synthetically distorted images in the Waterloo Exploration Database [48] with a corpus of realistically distorted images from SPAQ [49].”
TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment (2023, methodology): “I, images from three datasets, SPAQ, AVA, and FLIVE, need to be resized.”
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives (2022, background): “, blurs, artifacts) and their impact on quality, so as to compare and guide technical systems such as cameras [14, 15], restoration algorithms [16–18] and compression standards [19].”
MUSIQ: Multi-scale Image Quality Transformer (2021, methodology): “It consistently achieves the state-of-the-art performance on three technical quality datasets: PaQ-2-PiQ [41], KonIQ-10k [16], and SPAQ [11], and is on-par with the state-of-the-art on the aesthetic quality dataset AVA [29].”
VBench: Comprehensive Benchmark Suite for Video Generative Models (2023, methodology): “Imaging quality refers to the distortion (e.g., over-exposure, noise, blur) presented in the generated frames, and we evaluate it us-ing the MUSIQ [57] image quality predictor trained on the SPAQ [24] dataset.”
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second (2024, unknown): “SPAQ (Fang et al., 2020) provides casual photographs from mobile phones.”
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels (2023, background): “…processing (Sheikh et al., 2005), to directly addressing user-generated content (Tu et al., 2021a) ( e.g. photos and videos taken with smartphones (Fang et al., 2020)), and moving on to the recently popular AI-generated content (Li et al., 2023), at every stage, accurately evaluating visual…”