Mobile phone camera benchmarking: combination of camera speed and image quality

Peltoketo, Veli-Tapani

doi:10.1117/12.2034348

Cited by 1 publication

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sharpness, color reproduction, and noise have been regarded as the most important metrics of imaging quality (e.g., [62]), but no single metric exists that would depict the quality of a camera as a whole. Firstly, sharpness determines the amount of detail the imaging sensor can capture.…”

Section: Image Qualitymentioning

confidence: 99%

“…To achieve this, we recognized that geometric means have been described as suitable averaging approaches to combine a wide range of measurements even without normalization [103,104]. For example, [62] used geometric means to combine camera phone image quality and performance metrics into one benchmark score. Similar to this, the point cloud colorization quality score per scan was calculated as a geometric mean of the selected quality metrics using the following Equation (1):…”

Section: Combining Metrics For a Quality Scorementioning

confidence: 99%

“…Numerous image quality studies have focused on testing digital cameras in general (e.g., [59,60]), or on more specific types of devices and systems such as camera phones (e.g., [61,62]), 360-degree cameras (e.g., [63,64]), and digital aerial imaging sensors (e.g., [65][66][67]). However, the authors have been unable to identify any studies concerning the imaging quality of TLS systems with integrated cameras.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the Quality of TLS Point Cloud Colorization

et al. 2020

View full text Add to dashboard Cite

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

show abstract

Section: Image Qualitymentioning

confidence: 99%