Huawei P50 Pro Selfie test

Exposure and color are the key attributes for technically good images. For exposure, the main attribute evaluated is the brightness of the face in various use cases and lighting conditions. Other factors evaluated are contrast and dynamic range, eg. the ability to make details visible in both light and dark areas of the image. Repeatability is also important because it demonstrates the camera’s ability to deliver the same rendering when shooting consecutive images in a row.
For color, the analyzed image quality attributes are skin tone rendering, white balance, color shading, and repeatability.

Autofocus tests evaluate the accuracy of focusing on the subject’s face, the repeatability of precise focus, and depth of field. While a shallow depth of field can be nice for a single-subject selfie or close-up shot, it can be problematic in specific conditions like group selfies; both situations are tested. The accuracy of the focus is also evaluated in all real images taken, from 30 cm to 150 cm, and in low light conditions up to the outdoors.

Texture tests analyze the level of detail and texture of subjects in images taken in the laboratory as well as in real-life scenarios. For natural shots, special attention is paid to the level of detail in the facial features, such as the eyes. Objective measurements are performed on map images taken under various lighting conditions from 1 to 1000 lux and different types of dynamic range conditions. The charts used are the proprietary SBMARK (DMC) chart and the Dead Leaves chart.

Noise tests analyze various noise attributes such as intensity, chromaticity, grain and texture on real life images as well as graphical images taken in the laboratory. For natural images, special attention is paid to noise on faces, but also on dark areas and in conditions of high dynamic range. Objective measurements are performed on graphical images taken under various conditions from 1 to 1000 lux and different types of dynamic range conditions. The graph used is the SBMARK Dead Leaves graph and standardized measurement such as visual noise derived from ISO 15739.

Artifact evaluation examines lens shading, chromatic aberrations, distortion measurement on the dot and MTF chart, and ring measurements on the SFR chart in the lab. Particular attention is paid, among other things, to ghosting, quantization, halos and variations of shades on the face. The more severe and frequent the artifact, the greater the point deduction from the score. The main artifacts observed and the corresponding point loss are listed below.

Bokeh is tested in a dedicated mode, usually in portrait or aperture mode, and analyzed by visually inspecting all images captured in the lab and under natural conditions. The goal is to reproduce portrait photography comparable to that taken with a DSLR and a wide aperture. The main image quality attributes to look out for are depth estimation, artifacts, blur gradient, and bokeh blur spotlight shape. The attributes of the portrait image quality (exposure, color, texture) are also taken into account.

SBMARK engineers capture and evaluate more than 2 hours of video in controlled laboratory environments and natural scenes in low light conditions, indoors and outdoors, using the default settings of the front camera. The assessment consists of visually inspecting natural videos taken under various conditions and carrying out objective measurements on graphical videos recorded in the laboratory under different conditions from 1 to 1000+ lux and color temperatures from 2,300 K to 6,500 K.

Exposure tests evaluate facial brightness and dynamic range, eg. the ability to make details visible in both light and dark areas of the image. The stability and temporal adaptation of exposure are also analyzed. Image quality color analysis examines skin tone rendition, white balance, color shading, white balance stability, and its adaptation when the light changes.

Consistency tests analyze the level of detail and texture of real-life videos, as well as graph videos recorded in the lab. Natural video recordings are evaluated visually, with particular attention to the level of detail of the facial features. Objective measurements are performed on images of graphs taken under various conditions from 1 to 1000 lux. The chart used is the Dead Leaves chart.

Noise tests analyze various noise attributes such as intensity, chromaticity, grain, texture, temporal aspects on real-life video recordings and graph videos taken in the laboratory. Natural videos are evaluated visually, with particular attention to noise on faces. Objective measurements are performed on card videos recorded under various conditions from 1 to 1000 lux. The graph used is the SBMARK visual noise graph.

The stabilization assessment verifies the device’s ability to stabilize footage using software or hardware technologies such as OIS, EIS, or any other means. The assessment examines overall residual motion on the face and background, smoothness and gelatin artifacts, during walking and panning use cases under various lighting conditions. The video below is an excerpt from one of the tested scenes.

Artifacts are evaluated with MTF and ring measurements on the SFR graph in the lab, as well as frame rate measurements using the Universal Timer LED. Natural videos are visually evaluated with special attention to artifacts such as quantization, hue shift, and face rendering artifacts, among others. The more severe and frequent the artifact, the greater the point deduction from the score. The main artifacts and the corresponding point loss are listed below