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How a Professional Landscape Photographer Edits an Image From Raw to Print

How a Professional Landscape Photographer Edits an Image From Raw to Print

The vast majority of landscape photography involves substantial editing to create a final image, and if you are new to the genre, it can be helpful to see how a professional goes about the process. This great video tutorial features a landscape photographer editing a photo from raw file to print. 

Coming to you from Nigel Danson, this fantastic video tutorial features him guiding us through an edit of an image from the raw file to the print. The edit is the chance to add a lot of your personality and creative ideas to a landscape image through a wide range of techniques. The important thing to remember is that it can be very easy to go overboard when editing. I like to zoom out quite a bit fairly often just to get a quick overview of the image as I am working. Also, when you are done editing, try to step away from your monitor for a minute, then come back and take one last look at it before exporting; you will often want to make a few final adjustments after you do this. Check out the video above for the full rundown from Danson. 

And if you really want to dive into landscape photography, check out “Photographing The World 1: Landscape Photography and Post-Processing with Elia Locardi.” 

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How to Add Textures and Overlays to an Image Using Photoshop

How to Add Textures and Overlays to an Image Using Photoshop

Beyond technical applications like retouching, Photoshop is a fantastic place to really explore your creativity by pushing your edits further. Using textures and overlays is one way to enhance your edits, and this great video tutorial will show you how it is done. 

Coming to you from Aaron Nace with Phlearn, this excellent video tutorial will show you how to use Photoshop to add textures and overlays to your photos. Adding a texture or overlay to a photo can be a fantastic way to really liven it up and to explore your creativity. Luckily, it is not particularly hard to do either, making it easy to quickly explore different overlays. One great thing to do is to build your own library of textures and overlays. For example, you can build a bokeh library pretty easily simply by defocusing your lens and shooting things like holiday lights in the dark. Of course, when you are doing something like adding a bunch of bokeh to an image, it can easy to take it a bit too far, so be sure to step away for a second when you are done and re-evaluate the final image with a fresh set of eyes. Check out the video above for the full rundown from Nace. 

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See How a Landscape Photographer Edits an Image From Start to Finish

See How a Landscape Photographer Edits an Image From Start to Finish

Of all the genres of photography, landscape photography might be the one in which your final image depends on the edit the most. With such a wide range of editing techniques and creative styles, it can be helpful to see how a photographer edits one of their photos from start to finish, and this great video will show you exactly that. 

Coming to you from Matt Kloskowski, this excellent video will show you how he edits a landscape photo from start to finish. Most tutorials focus on a single technique, and there is certainly nothing wrong with that, but given how you will often apply several techniques and ideas to any single photo to produce a finished product, it can be really helpful to see how a photographer works from beginning to end. Of course, with the often heavy amount of editing that can go into landscape work, it can be very easy to go overboard. Be sure to zoom out often and step away for a moment or two after you finish the edit before you export so you can evaluate it with fresh eyes. Check out the video above for the full rundown from Kloskowski.

And if you really want to dive into landscape photography, check out “Photographing The World 1: Landscape Photography and Post-Processing with Elia Locardi.” 

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First Image Leaked of the ‘Sony a7c’: A Budget Full Frame Mirrorless Camera

First Image Leaked of the 'Sony a7c': A Budget Full Frame Mirrorless Camera

It seems like the competition for the mirrorless crown has only just begun. Canon produced the first super affordable, full frame camera, with its EOS RP. Even Panasonic is trying to get in on the entry level side of the industry with its Lumix S5. Sony, seemingly doesn’t want to be left out of the fray. 

Coming from Sony Alpha Rumors, a new image has been leaked and it’s presumably of an upcoming mirrorless camera from Sony. This camera is thought to be called the a7c and will be a budget full frame camera. This is great news for the industry because competition ultimately serves the consumer. If the price of full frame camera continue with this down trend, I think a lot of photographers are going to be happy. 

The only thing that I’m not too keen on is just how ugly this potentially upcoming camera is. It reminds me of cheap electronic devices from the 90s. I mean I understand that companies need to do what they can to produce an inexpensive option, however, a different color could have helped. 

Nonetheless, if this is true, this is quite obviously good news, especially when you look at the spec sheet. Sure, it doesn’t have the best and highest end specs but the IBIS and 4K video features are probably going to make this an extremely popular option. Not to mention the fact that it may have the same sensor as the much loved a7 III

Check out the Sony Alpha Rumors website to see the full rumored specifications. 

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Why perceptual evaluation is essential for image quality testing

Why perceptual evaluation is essential for image quality testing

Objective lab-based image quality testing methods used by DXOMARK and many manufacturers and other organizations in the mobile and imaging industries have become more and more sophisticated over the years, allowing us to assess a broad array of image quality attributes in a controlled and repeatable environment.

However, there are reasons why it makes sense to complement objective lab testing with additional methods to achieve even better quality results.

Modern mobile imaging systems are very complex, incorporating more and more content aware processing. Consequently, image results depend a lot more on the content of the scene than used to be the case for conventional cameras in the past. For example, machine learning technology can be used to detect the subject in a scene and improve AF tracking capabilities, especially when it comes to pets. It is therefore important to assess the image quality using as wide a set of scenes as possible to cover as many use cases as possible. A broad array of different conditions can be created in lab settings (type of light, lux levels, scene content); however, real-life situations are infinitely more varied than even the most sophisticated lab setups.

Objective measurements are designed to analyze a well-defined set of attributes. However, every new device generation introduces new image processing algorithms and technologies, and image results can include unpredictable elements that are difficult to anticipate. For example, ghosting artifacts through frame stacking or spurious loss of texture are examples of artifacts we have seen only on devices from the most recent generations. It is impossible to design objective tests in advance for those new artifacts, so we still need alternative methods to spot those unpredictable occurrences.

Most of the time the results of our objective tests are representative of a real-life experience, but on those occasions where this is not (or is not entirely) the case, perceptual assessment allows us to complete the picture. Perceptual testing complements objective lab testing and ensures that all unpredictable camera behavior is detected. It also allows us to cover more scenes and shooting conditions, widening our test protocol and allowing us to capture and analyze more image quality data. Perceptual testing essentially makes the DXOMARK Camera test protocol even more robust and reliable.

What is perceptual testing?

Let’s start by saying what it is not: perceptual analysis is not the same as subjective analysis. Rather, perceptual analysis is the evaluation of image quality attributes by human operators, using a stringent methodology to ensure unbiased results that are of equal quality to those obtained through objective testing methods. At DXOMARK, all perceptual evaluation is undertaken by engineers and technicians who are image quality experts and have years of experience in the field.

DXOMARK’s perceptual analysis methodology includes two components:

  • The shooting protocol defines which scenes to shoot for our testing and exactly how to capture the images of each scene.
  • The analysis protocol defines which image quality features to analyze and exactly how to perform the analysis.

These protocols have been designed to meet the following requirements:

  • Neutrality: results of perceptual analysis have to to be independent of the human operator—that is, different operators have to produce identical results when evaluating the same device, and all devices go through exactly the same testing and analysis procedures.
  • Relevance: perceptual analysis has to focus on image quality aspects that are relevant to consumers and photographers.
  • Reliability: perceptual analysis has to be independent of shooting conditions such as weather or light conditions and reliably deliver consistent results.
  • Comprehensiveness: perceptual analysis has to include all image quality attributes that are necessary for evaluating the device under test.
Why perceptual evaluation is essential for image quality testing 1

For our perceptual testing, our image quality experts compare and judge images from the device under test against images from a range of reference devices.

Examples

So while objective testing provides a lot of information about camera image quality, we need additional perceptual testing to cover unpredictable camera behavior, widen the test protocol in order to include as many shooting situations as possible, and ultimately make the DXOMARK Camera scores even more relevant. Let’s take a look at a few examples of image quality attributes where perceptual testing is used to complement objective test results.

Exposure

To objectively test exposure, we use a range of test charts in our lab to reproduce as many shooting scenarios as possible under controlled conditions. We take measurements using a range of different light levels, from almost complete darkness to very bright, and with several types of light sources, simulating daylight, tungsten, and fluorescent illumination.

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DXOMARK exposure test charts

Our set of lab test charts for exposure covers many typical use cases with various lighting conditions and levels of contrast. We continuously work on expanding the scope of our objective testing; however, it is impossible to anticipate every possible lighting situation in the lab, which is why we designed a complementary perceptual shooting plan to assess camera performance in challenging high-contrast outdoor scenes or for backlit portraits, among others.

We evaluate outdoor and indoor exposure using DXOMARK’s extensive perceptual database of real-life scenes, all of which require following precise shooting and framing instructions.

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Example shots from the DXOMARK outdoor database

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Example shots from the DXOMARK indoor database

Let’s have a closer look at some samples to illustrate how perceptual evaluation complements our objective tests. In the graph below, you can see the results of the objective exposure tests for the Apple iPhone XS Max. Results are exactly on target in bright light and under indoor light conditions, with some underexposure measured only in low light.

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Apple iPhone XS Max, exposure analysis

The results of the objective test above are pretty much confirmed by the real-life results in our perceptual database. The XS Max delivers accurate exposure in almost any outdoor and indoor situation we have tested, as in the three examples below.

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Apple iPhone XS Max, good outdoor exposure

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Apple iPhone XS Max, good indoor exposure

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Apple iPhone XS Max, good indoor exposure

However, using our perceptual methods we also found that the XS Max exposure system can struggle in certain unusual and challenging scenarios — for example, shaded foreground subjects in front of a bright background. In the outdoor samples below, the subject is in the shade and occupies a fairly small portion of the frame. In the background there is a bright sky and a distant secondary subject (the Eiffel Tower). This is a difficult scene to deal with for any exposure system, but the Samsung Galaxy Note 10+ 5G handles it visibly better than the iPhone, achieving much better exposure on the subject in the foreground.

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Apple iPhone XS Max, underexposed foreground subject

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Samsung Galaxy Note 10+ 5G, good exposure on foreground subject

The situation is similar for the indoor scene below. As in the previous sample, the camera has to deal with backlit foreground subjects. In this case, however, the subjects take up more space in the frame. It is difficult to achieve good exposure on the subjects without clipping large portions of the bright background. But as you can see, the Huawei P30 Pro deals better with this challenge than the XS Max.

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Apple iPhone XS Max, underexposed foreground subject

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Huawei P30 Pro, good exposure on foreground subject

The two exposure samples share a similar scene composition that is not covered in any standard lab testing, but we can still detect exposure issues like the one illustrated above thanks to perceptual testing.

Color

For our objective tests of color, we use a calibrated ColorChecker chart. After capturing our test images, we measure the tint and saturation of the 18 colored patches and check for white balance casts using the six neutral patches at the bottom of the chart, with results presented in an ellipsoid format. The best results for saturation, tint, and white balance are located inside the green ellipsoid; the worst results are plotted outside the red one.

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ColorChecker® calibrated chart

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White balance results for different types of light source presented in an ellipsoid format

Test charts like the ColorChecker can include only a limited number of colors, which is why we use real-life scenes to complement our objective testing and expand the amount of data we captured and analyze.

Like for exposure, objective color tests are in line with perceptual testing results most of the time. In the example below, you can see that most data points for the Samsung Galaxy S10+ in bright light are plotted within the green ellipsoid, meaning that the lab test images show neutral white balance. This is confirmed by the real-life samples from the DXOMARK perceptual database.

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Samsung Galaxy S10+, neutral white balance in objective testing

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Samsung Galaxy S10+, neutral white balance

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Samsung Galaxy S10+, neutral white balance

However, occasionally results from objective and perceptual testing do not fully align because the lab scene cannot cover all real-life scenarios. For example, the white balance graph for the Apple iPhone XS Max shows that greenish white balance casts are visible in bright light, but this is not always noticeable in real-life shots. The late-afternoon outdoor portrait shows a slightly warm but acceptable cast, and the image of the Eiffel Tower is quite neutral.

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Apple iPhone XS Max, color casts in objective testing

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Apple iPhone XS Max, neutral white balance

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Apple iPhone XS Max, neutral white balance

Mixed lighting situations are another complex scenario that many cameras find difficult to deal with, so using perceptual testing to broaden the scope of testing and include several mixed-lighting scenes is a good way of making sure DXOMARK results match the real-life experience.

Autofocus

We undertake our objective DXOMARK Autofocus tests in the lab using a custom-built setup that includes a Dead Leaves chart as the focus target, as well as a motorized refocus trigger that we synchronize with a digital camera trigger and a universal timer. We place the refocus trigger between the camera and the focus target to defocus between shots, then move the refocus trigger out of the way for the test itself. We program the refocus trigger to shoot after a delay of 500ms, and we repeat the same test in low-light conditions with a 2000ms delay.

Why perceptual evaluation is essential for image quality testing 21

The test is designed to measure how long it takes a camera to acquire focus, the time it takes to focus, and the repeatability of the focus. We perform these tests using several types of illuminants and at various light levels.

For our autofocus tests, perceptual testing allows us to cover additional shooting situations, with varying subject distances, lighting directions, and dynamic ranges, among other parameters.

Let’s look at a few examples. Below you can see the objective autofocus test results for the Xiaomi Mi CC9 Pro Premium Edition, the Huawei Mate 30 Pro, and the Samsung Galaxy Note 10+ 5G in bright light.

Why perceptual evaluation is essential for image quality testing 22

All three devices in this comparison produce excellent autofocus results.

As you can see, all three devices perform very well, producing sharp results with only a very minimal delay (less than 100ms) after triggering the shutter. Perceptual testing also allows us to detect those edge cases where the systems don’t function perfectly. For example, with the subjects at a longer distance from the camera, such as in the samples below, the Mi CC9 Pro Premium Edition has a tendency to focus on the background. The effect is slightly exacerbated by the device’s large image sensor and therefore comparatively narrow depth of field. The Mate 30 Pro and the Samsung Note 10+ both render the subjects sharp and in focus.

Xiaomi Mi CC9 Pro PE, focus at long distance

Why perceptual evaluation is essential for image quality testing 24

Xiaomi Mi CC9 Pro Premium Edition, crop, focus on background

Huawei Mate 30 Pro, focus at long distance

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Huawei Mate 30 Pro, crop, good focus on subjects

Galaxy Note 10+ 5G, focus at long distance

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Samsung Galaxy Note 10+ 5G, crop, good focus on subjects

Errors can also occur at close distance. When capturing the scene below, the Xiaomi and Samsung cameras focus correctly on the subjects; but interestingly, in this particular situation, the Huawei Mate 30 Pro camera focused on the background rather than on the couple in the front. Overall, it’s fair to say that good results in the lab are a good indicator for good overall autofocus performance, but failures can still occur in specific challenging use cases.

Xiaomi Mi CC9 PE, focus at close distance

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Xiaomi Mi CC9 Pro Premium Edition, crop, good focus on subjects

Huawei Mate 30 Pro, focus at close distance

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Huawei Mate 30 Pro, crop, subjects slightly out of focus

Galaxy Note 10+ 5G, focus at close distance

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Samsung Galaxy Note 10+ 5G, crop, good focus on subjects

Other challenging scenarios include complex (and potentially moving) subjects, such as pets, combined with difficult high-contrast or backlit scenes (for example); and group scenes where depth of field can come into play, and when the focus point should keep as many subjects in focus as possible.

The group shot below was captured with an Asus ZenFone 6, which focused on the person closest to the camera; as a result, the subject at the back of the group is out of focus. It would have been a better strategy to focus on the subject that is second-closest to the camera to keep as many elements as possible in focus. In the image on the right, captured with a Samsung Galaxy Note 10+ 5G, we can see that the focus system was confused by the very complex scene and focused on the brighter background instead of the pet in the foreground.

Asus ZenFone 6, group shot

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Asus ZenFone 6, crop, subject at the back out of focus

Samsung Galaxy Note 10+ 5G, backlit scene with pet

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Samsung Galaxy Note 10+ 5G, crop, subject out of focus

Texture and Noise

We also use the Dead Leaves chart for texture and noise measurements in the lab. We measure texture on the actual dead leaves pattern and measure noise on the surrounding gray level patches, taking measurements at light levels from 1 to 1000 lux and using a variety of light sources. However, as varied as these test conditions are, there still are plenty of use cases that are not covered—for example, texture in high-contrast scenes or on moving subjects, as well as noise on textured and colored image areas (to name a few).

Let’s have a look at an example for texture evaluation. Our lab measurements show that the Xiaomi Mi CC9 Pro Premium Edition has higher levels of detail than the Huawei Mate 30 Pro and the Samsung Galaxy Note 10+ 5G in bright and medium light:

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Comparison of objective test results for texture

Looking at the real-life samples below, we can see that the Xiaomi device does indeed render noticeably better detail than the Huawei and the Samsung. However, the difference between the Mate 30 Pro and the Note 10+ 5G in bright light is bigger than the objective results suggest. In this instance, the objective test results have provided the right order among the comparison devices, but for the specific sample scene below, not the scale. By complementing objective tests with perceptual evaluation, we can fine-tune the results and take into account analyses from a much wider range of scenes.

Xiaomi Mi CC9 Pro Premium Edition, outdoor detail

Why perceptual evaluation is essential for image quality testing 41

Xiaomi Mi CC9 Pro Premium Edition, crop, best detail

Huawei Mate 30 Pro, outdoor detail

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Huawei Mate 30 Pro, crop, second-best detail

Samsung Galaxy Note 10+ 5G, outdoor detail

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Samsung Galaxy Note 10+ 5G, crop, lowest level of detail

HDR scenes are another good example for a type of scene where perceptual testing contributes to making test results more robust and reliable. Current objective tests for texture are geared towards low- to medium-contrast scenes. Texture results for HDR scenes can be very different than for lower-contrast scenes, however, which is why we use perceptual tests to complement objective tests. In the sample below, you can see that the Huawei P30 Pro captures much higher levels of detail on the face of the model in the low-contrast indoor shot than in the backlit high-contrast image.

Huawei P30 Pro, indoor texture

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Huawei P30 Pro, crop, very good detail

Huawei P30 Pro, HDR scene, indoor detail

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Huawei P30 Pro, crop, loss of detail

Similar situations exist for noise testing as well. The objective test results for noise below show that the Honor V30 Pro and Mate 30 Pro images have lower noise levels than the Samsung Galaxy Note 10+ 5G in almost all light conditions except very bright light.

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Comparison of objective test results for noise

The objective results are confirmed by many scenes in our perceptual database, such as the indoor shot below. The Honor and Huawei show similarly low noise levels in this scene, while the Samsung produces noticeably more noise.

Honor V30 Pro, indoor noise

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Honor V30 Pro, crop, low noise

Huawei Mate 30 Pro, indoor noise

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Huawei Mate 30 Pro, crop, low noise

Samsung Galaxy Note 10+ 5G, indoor noise

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Samsung Galaxy Note 10+ 5G, crop, higher noise

All three devices show low noise levels in bright light, which again is in line with the objective test results. However, the Samsung camera has slightly more noise in the shadow areas of this high-contrast scene.

Honor V30 Pro, noise in shadow areas

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Honor V30 Pro, crop, low noise

Huawei Mate 30 Pro, noise in shadow areas

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Huawei Mate 30 Pro, crop, low noise

Galaxy Note 10+ 5G, noise in shadow areas

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Samsung Galaxy Note 10+ 5G, crop, more noise

Moving subjects are another use case that is difficult to reproduce in in the lab. In the image below, you can see that the Apple iPhone 11 Pro Max image shows stronger noise on the moving cyclist than on the static elements of the scene. There are multiple possible explanations for this, most of them most likely linked to the need for faster shutter speeds to freeze the motion in the scene.

Apple iPhone 11 Pro Max, noise on moving subject

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Apple iPhone 11 Pro Max, crop, less noise on static elements

Conclusion

So what should you take away from this article? Well, principally, that objective testing is very accurate and efficient, but perceptual testing helps make image quality testing even more reliable and robust by expanding the number of test scenes and providing the ability to detect unexpected or thus far unknown camera behavior. It is this essential combination of objective and perceptual testing that ensures that our test results are as relevant as possible to smartphone users all over the globe.

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