Author Archives: DXOMARK

Nowadays, cameras are widely used to detect potential obstacles for driving assistance. The safety challenges have pushed the automotive industry to develop a set of image quality metrics to measure the intrinsic camera performances and degradations. However, more metrics are needed to correctly estimate computer vision algorithms performance, which depends on environmental conditions. 

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA).

High Dynamic Range (HDR) videos attract industry and consumer markets thanks to their ability to reproduce wider color gamuts, higher luminance ranges and contrast. While the cinema and broadcast industries traditionally go through a manual mastering step on calibrated color grading hardware, consumer cameras capable of HDR video capture without user intervention are now available. 

R. Falcon, M. Patti, S. Brochard-Garnier, G. Pacianotto Gouveia, S. Torres Acevedo, T. Bergot, R. Alarcon, C. Bomstein, H. Macudzinski, P. Maitre, L. Chanas, H. Nguyen, B. Pochon, F. Guichard Videoconferencing has become extremely relevant in the world in the past few  years. Traditional image and video quality evaluation techniques prove to be insufficient to […]

Cameras, especially camera phones, are using a large diversity of technologies, such as multi-frame stacking and local tone mapping to capture and render scenes with high dynamic range. ISO-defined charts for OECF estimation and visual noise measurement are not really designed for these specific use cases, especially when no manual control of the camera is […]

We tackle the issue of estimating the noise level of a camera, on its processed still images and as perceived by the user. Commonly, the characterization of the noise level of a camera is done using objective metrics determined on charts containing uniform patches at a given condition. 

We propose a novel and standardized approach to the problem of camera-quality assessment on portrait scenes. Our goal is to evaluate the capacity of smartphone front cameras to preserve texture details on faces. We introduce a new portrait setup and an automated texture measurement. The setup includes two custom-built lifelike mannequin heads, shot in a […]

We present an objective metric for quantifying the amount of flare of the lens of a camera module. This includes hardware and software tools to measure the spread of the stray light in the image. A novel measurement setup has been developed to generate flare images in a reproducible way via a bright light source, […]

We propose a comprehensive objective metric for estimating digital camera system performance. Using the DXOMARK RAW protocol, image quality degradation indicators are objectively quantified, and the information capacity is computed. The model proposed in this article is a significant improvement over previous digital camera systems evaluation protocols, wherein only noise, spectral response, sharpness, and pixel […]

We describe image quality measurements for HDR scenes covering local contrast preservation, texture preservation, color consistency, and noise stability. By monitoring these four attributes in both the bright and dark parts of the image, over different dynamic ranges, we benchmarked four leading smartphone cameras using different technologies and contrasted the results with subjective evaluations.

We propose a method to quantitatively evaluate the quality of computational bokeh in a reproducible way, focusing on both the quality of the bokeh (depth of field, shape), as well as on artifacts brought by the challenge to accurately differentiate the face of a subject from the background, especially on complex transitions such as curly […]

This paper presents laboratory setups designed to exhibit the characteristics and artifacts that are peculiar to Multi-Image technologies. We also propose metrics towards the objective and quantitative evaluation of those artifacts.