Details

Autor(en) / Beteiligte

• Han, Zongxi
• Liu, Yutao
• Xie, Rong

Titel

A large-scale image database for benchmarking mobile camera quality and NR-IQA algorithms

Ist Teil von

• Displays, 2023-01, Vol.76, p.102366, Article 102366

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Camera-oriented quality assessment (CQA) differs from traditional image quality assessment (IQA) in that “distorted” images are straight out of real devices instead of various types and levels of artificial operations. However, despite its value for both customers and manufacturers, academic and industrial fields, there are few CQA databases created years ago. To reflect recent mobile imaging advancements, we present a new massive Phone Camera Benchmarking database (PCB2021) in this paper. In PCB2021, 40 modern phone units featuring photography are simultaneously compared in 182 scenes for a total of 7280 images, which can be classified into six categories (sub-datasets) based on different focal lengths and user cases: main camera (107), ultra-wide (20), 2×, 3×, 5× zoom lenses (26, 8, 10) and night-mode (11). The shooting process begins from 7:00 am to 11:00 pm and lasts more than a month. In the subjective study, to overcome the high-resolution-induced overall quality evaluation difficulty, five image attributes: exposure/contrast, color, sharpness, graininess, artifacts are assessed separately on each dataset. To reduce ranking complexity for large-scale cameras, a dynamic anchor ruler method is proposed to obtain quality orders efficiently. With the constructed PCB2021, we further evaluate 15 mainstream no-reference (NR) IQA algorithms. The finding is that for zoomed images, sharpness metrics can achieve Spearman correlation coefficients above 0.8, while for the subtle main camera and night-mode images, performances of all fifteen algorithms drop down quickly, i.e. 0.1-0.2 for the former and ∼0.5 for the latter. The entire database, expert rankings and algorithm performance reports will be freely available on request. •To faciliate authentic image quality assessment, a large-scale camera-oriented quality assessment database is constructed, which includes 40 mobile cameras, 182 test scenes under six sub-categories. The selected categories also reflect mobile imaging advancements such as zoom lenses and night mode.•Separate quality attributes, expert ranking, and a dynamic anchor ruler method are used or proposed to tackle the high resolution, narrow quality range, and scale difficulties in the subjective study, respectively.•Fifteen no-reference image quality algorithms are tested, showing that sharpness metrics have decent correlations in the zoomed image database, while in the more subtle main (color and exposure) and night-mode image databases, performances of all algorithms fall down quickly (i.e. 0.1-0.2 for the former and 0.5 for the latter).