But what is Image Quality (IQ), how do you define it, and is it possible to assess “quality” as objectively as possible? Fortunately, it is possible by looking at major objective image quality metrics. Objective metrics means that we can say that “more” or “less” of a specific metric is a “good” or “bad” thing.
- Objective Positive metrics
- [Preservation of] Detail, texture
- Color fidelity
- [Preservation of] brightness, contrast and shadows
- Objective Negative metrics
- Image distortion, chromatic aberrations, or lens flares
Objective Image Quality Attributes
Positive image quality attributes
Positive attributes can be defined as image quality enhancers meaning that the more you have and the better it is. Various camera image quality tests try to quantify these attributes.
Detail, Texture, Sharpness, Resolution, Readability
Everyone agrees that capturing as much detail as possible is a crucial thing for cameras. This is something that can be objectively assessed to compare different camera capture.
The perception of sharpness will vary depending on the output device you’re using. Mobile phones, large TV screens, or printed content are different outputs and require different levels of details to achieve a seemingly perfectly sharp image.
Greater detail also gives you higher photo quality after cropping pictures. Cropping is a popular technique used to re-frame photos, and people use it all the time.
The Texture preservation of various materials appearance (like the toys’ fur below) is really important and reveals several skills such as proper exposure, sharpness and filtering, for example. In the zoom photo below, the one on the right doesn’t quite capture the real texture of the plush toy, because its camera is not powerful enough.
In the example below, the tree bark and house siding are losing a lot of Texture in the photo crop to the right:
Photographers expect cameras to capture the scene they are looking at with high fidelity. No two cameras capture the same photos because there’s a very long chain of image processing and filters.
But some do a better job than others. At the top, cameras such as professional DSLRs capture images and colors that resemble what the photographer is looking at.
The higher the fidelity, the more “controllable” the camera becomes because you know in advance what you’re going to get. The farther away you go from “what you see”, and the less controllable the camera experience becomes.
Lighting is one of the most important aspects of photography. Two of the most crucial lighting attributes are Dynamic Range and Brightness/Contrast, and they are intertwined.
The dynamic range determines how much detail is captured from the brightest and darkest areas. This is very difficult because mobile camera sensors often cannot cover the real world’s full dynamic range. That’s why photos sometimes have washed out areas and overly dark spots.
Various high-dynamic-range (HDR) techniques have been created and improved, both for sensors and image processing levels. HDR data is often captured in several frames, or layers, and the merging into the final photo can be more or less successful. Sometimes, contrast and shadows can be destroyed by excessive image filtering or overly aggressive HDR techniques.
Ideally, HDR techniques would even allow for capturing details that are not immediately visible to the naked eye, but that could be revealed later during photo editing.
The preservation of Brightness, Contrast and Shadows is very important because they largely contribute to a scene’s mood and should ideally be captured with as much fidelity as possible.
Note that “preserving” brightness and shadows, doesn’t mean “turning night into day”. when it comes to night photography, brighter is not always better.
Negative image quality attributes
Camera users can also easily notice some negative/undesired things in photos. They are camera photo quality reducers.
Noise can negatively affect the appearance of photos with a visible grainy appearance, which affects the general quality but also muddies some fine details.
Artifacts are small details were not originally in the scene but often appear as an unwanted result of image filtering. For example, we had cases where random pink dots appeared in the image when specular highlights were extreme.
Many people are also familiar with the white halo that appears when images are overly filtered for artificial sharpening. (photo credit: photographystepbystep)
There are also low-key (for consumers) attributes such as image distortion, chromatic aberrations, or lens flares, which can be introduced by some lenses.
What Image Quality Is Not
I might help to also talk about what image quality is not. There is a vast difference between “quality” (data-quality) and “aesthetics” (art).
Quality is not Style or Aesthetics. You can think of photo style and aesthetics as Instagram filters: they transform the image in an artistic way that could please (or displease) your taste, but they don’t fundamentally alter the photo’s quality attributes.
Instagram filters are a great example of aesthetics vs. quality. These filters can make an image more pleasing to YOUR taste, but they don’t increase the original image capture quality. They only change the “style” of the photo.
It is lovely if a particular style looks pleasing to YOU, but it doesn’t mean that the image’s quality is “higher.”
Similarly, you might genuinely love McDonald’s burger patties more than a juicy steak, but it doesn’t mean the burger patty has a higher meat “quality”.
In our camera reviews, we often illustrate the visual differences between the real world and the camera shots so everyone can decide for themselves about the style they prefer.
Many objective elements in photography contribute to genuine camera Image Quality (IQ), and that’s what we are looking at when we review cameras. They are measurable and we know if it is good or bad to have more or less of them in a photo. We want more of the positive attributes, and less of the negative ones.
How we combine, weigh, and prioritize these elements to form a final benchmark score is the most crucial part of obtaining a helpful ranking. We also publish meaningful sub-scores to clarify the review further.
We regularly talk to consumers and mobile camera reviewers to come up with HardWare and IQ metrics that match users sentiment.
For example, our hardware-driven Uber HardWare Camera score analysis matched the experts’ sentiments as for when Samsung and Huawei started to equal, and beat, Apple’s cameras. (Article #1, Article #2). No other hardware-based camera benchmark in the world can pick this up.
Our Uber Image Quality Camera benchmark’s clarity and simplicity forced benchmark competitors to scramble and add Night and Ultrawide photography scores, two fundamental categories ignored for years, until we came along.
In conclusion, Camera Image Quality can be rated based on objective metrics, and our goal is to help you get the best camera for your budget and goals. Thanks for your interest, and check our latest Mobile Camera Reviews.
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