We put the Google Pixel 7 through our rigorous SBMARK Selfie test suite to measure its performance in photos and videos from an end-user perspective. This article analyzes how the device performed in a variety of tests and several common use cases and aims to highlight the most important results of our tests with an excerpt of the data captured.

Overview

Main specifications of the front camera:

  • 10.8MP sensor
  • 1.22µm pixels
  • Lens with f/2.2 aperture
  • 4K video at 30/60 fps (4K at 30 fps tested)

Pros

  • Natural skin tones and good white balance, even in difficult conditions
  • Good exposure and wide dynamic range
  • Effective video stabilization
  • Wide enough depth of field
  • Fairly low noise levels in bright light and indoors

Versus

  • Slight loss of fine detail
  • Faces out of focus at close shooting distance
  • Image noise in low light conditions

In our tests, the Google Pixel 7 delivered strong front-facing camera performance, delivering identical results to the flagship Pixel 7 Pro in terms of color and exposure, and coming very close in many other test areas. However, there were some notable differences between the two devices as well.

Focusing, which was a sticking point in our Pixel 7 Pro test, was slightly worse at close range on our Pixel 7 test unit. As a result, textures weren’t rendered as well on the 7 as they were on the 7. Pro, both for photos and videos. Our testers also noticed notable differences in the bokeh mode. Unlike the Pro model, the Pixel 7 didn’t apply a blur gradient to the front of the scene (between camera and subject), which resulted in a lower bokeh score.

In video mode, some artifacts, such as ghosting, were a little more visible on the Pixel 7. Aside from these minor differences, the Google Pixel 7 still offered very reliable front-facing camera performance and is among the best options for enthusiasts. of selfies.

Google Pixel 7 selfie scores compared to the high-end

This graph compares overall SBMARK Selfie photo and video scores between tested and referenced devices. The average and maximum scores of the price segment are also indicated. The average and maximum scores for each price segment are calculated based on the SBMARK device database.

Trial summary

Learn about SBMARK Selfie Tests: For scoring and analysis, SBMARK engineers capture and evaluate more than 1,500 test images in both controlled laboratory environments as well as outdoor, indoor, and low-light natural scenes using default camera settings front. The photo protocol is designed with the user in mind and is based on typical shooting scenarios, such as close-ups and group selfies. Evaluation is performed by visually inspecting images Cons a natural scene reference and by performing objective measurements on laboratory-acquired graph images under various lighting conditions from 1 to 1,000+ lux and color temperatures from 2,300 K to 6,500 K. For more information about SBMARK Selfie test protocol, please click here. More details on how we rate smartphone cameras can be found here. The following section compiles the key elements of SBMARK’s exhaustive testing and analysis. Full performance evaluations are available upon request. Please contact us on how to receive a full report.

Photo

134

Huawei Mate 50 Pro

Huawei Mate 50 Pro

Google Pixel 7 photo scores compared to the high-end

Photo tests analyze image quality attributes such as exposure, color, texture and noise under various lighting conditions. Focus range and the presence of artifacts are also evaluated on all images acquired under controlled laboratory conditions and on real-life images. All of these attributes have a significant impact on the final quality of the images captured with the tested device and can help you understand the main strengths and weaknesses of the camera.

Exposure

ninety two

Improve

Highest score

Color

105

Improve

Highest score

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

Google Pixel 7: accurate exposure and color

Google Pixel 7 Pro: accurate exposure and color

Focus

83

Huawei Mate 50 Pro

Huawei Mate 50 Pro

Autofocus tests evaluate the accuracy of focusing on the subject’s face, repeatability of accurate 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. Focus accuracy is also evaluated in all real-life images taken, from 30cm to 150cm, and in low-light or outdoor conditions.

Google Pixel 7 – depth of field

Google Pixel 7 – slightly blurry

Google Pixel 7 Pro – depth of field

Google Pixel 7 Pro: Slightly better focus

Framework

53

Asus ZenFone 7 Pro

Asus ZenFone 7 Pro

Texture tests analyze the level of detail and texture of subjects in images taken in the lab as well as real-life scenarios. For natural shots, particular attention is paid to the level of detail of 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.

Evolution of the acuity of the texture with the level of illuminance

This graph shows the evolution of texture acuity with lux level for two holding conditions. The sharpness of the texture is measured on the Dead Leaves graph in the Close-up Dead Leaves configuration.

Google Pixel 7: The level of detail is slightly lower

Detail of Google Pixel 7 Pro

Google Pixel 7 Pro: good details

Noise

79

Huawei Mate 50 Pro

Huawei Mate 50 Pro

Noise tests analyze various noise attributes such as intensity, chromaticity, grain, and structure on real-life images as well as graph images captured in the lab. For natural images, special attention is paid to noise on faces, but also to dark areas and high dynamic range conditions. Objective measurements are performed on chart images captured 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 as Visual Noise derived from ISO 15739.

Evolution of visual noise with illuminance levels in portable conditions

This graph shows the evolution of the visual noise metric with lux level under palmar conditions. The Visual Noise metric is the average of the Visual Noise measurement across all patches of the Dead Leaves graph in the Close-up Dead Leaves configuration. The SBMARK visual noise measurement is derived from the ISO15739 standard.

Artifacts

89

Improve

Highest score

Artifact assessment examines lens shading, chromatic aberrations, distortion measurement on the Dot and MTF graph, and ringing measurements on the SFR graph in the lab. Particular attention is paid to ghosting, quantization, halos and hue variations on the face, among others. The more serious and frequent the artifact, the greater the deduction of points from the score. The main artifacts observed and the corresponding loss of points are listed below.

Major penalties for photographic artifacts

Bokeh

65

Apple iPhone 14 Pro Max

Apple iPhone 14 Pro Max

Bokeh is tested in a dedicated mode, usually portrait or aperture mode, and analyzed by visually inspecting all images captured in laboratory and natural conditions. The goal is to reproduce a portrait photograph comparable to one taken with a DSLR and a large aperture. The main image quality attributes paid attention to are depth estimation, artifacts, blur gradient, and bokeh blur reflector shape. Vertical image quality attributes (exposure, color, texture) are also taken into account.

Google Pixel 7: No blur gradients

Google Pixel 7 Pro – blur gradient

Apple iPhone 14 Pro – blur gradient

video

144

Apple iPhone 14 Pro Max

Apple iPhone 14 Pro Max

About SBMARK Selfie Video Tests

SBMARK engineers capture and evaluate more than 2 hours of video in controlled lab environments and low natural light scenes, indoors and out, using the default front camera settings. Evaluation consists of visually inspecting natural video taken under various conditions and performing objective measurements on lab-recorded graph video under various conditions from 1 to 1000+ lux and color temperatures from 2,300K to 6,500K.

Google Pixel 7 video scores compared to the high-end

Video tests analyze the same image quality attributes as still images, such as exposure, color, texture or noise, as well as temporal aspects such as speed, exposure uniformity and stability, white balance and autofocus transitions.

Exposure

81

Apple iPhone 14 Pro Max

Apple iPhone 14 Pro Max

Color

87

Apple iPhone 14 Pro Max

Apple iPhone 14 Pro Max

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

Framework

72

Asus ZenFone 6

Asus ZenFone 6

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

Evolution of the acuity of the texture with the level of illuminance

This graph shows the evolution of texture acuity with lux level for two holding conditions. The sharpness of the texture is measured on the Dead Leaves graph in the Close-up Dead Leaves configuration.

Noise

67

Xiaomi Mi 11 Ultra

Xiaomi Mi 11 Ultra

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

Evolution of spatial visual noise with level of illumination

This graph shows the evolution of spatial visual noise with lux level. Spatial visual noise is measured on the visual noise table in the video noise setup. The SBMARK visual noise measurement is derived from the ISO15739 standard.

Time evolution of visual noise with level of illumination

This graph shows the evolution of visual noise over time with lux level. Temporal visual noise is measured on the visual noise table in the video noise setup.

Stabilization

82

Improve

Highest score

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

Artifacts

86

Apple iPhone 12 mini

Apple iPhone 12 mini

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

Top penalties for video artifacts

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Philip Owell

Professional blogger, here to bring you new and interesting content every time you visit our blog.