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In China, it is a typical scenario that people stretch out arms taking selfies within less than three seconds and then retouching blemishes or adding filters for a half-hour before posting them on social media. Image editor apps are widely used in China, whose selfie consumers might be the most advanced in the world. According to the statistics, each user has more than 2 photo editing apps downloaded on their phone. Among them all, Meitu is undoubtedly the superstar in this realm.
Figure 1. Statistics: each user installed an average of 2.4 apps. (Image source: www.199it.com)
Figure 2. Meitu logo. (Image source: www.google.com)
Meitu starts off as a PC version of photo editor in 2008 and later launched at mobile devices in 2011. As a powerful photo editor to go viral, Meitu app has created a rich portfolio of offerings by integrating advanced photo imaging technologies into one platform and introduced them to everyday life through an intuitive interface. With a mission of making the world a more beautiful place, it has led the mainstream aesthetics trend and helped improve users’ social lives. That’s why after 10 years of growth, facing with newborn competitors, it is still the dominating image processing apps in China and worldwide. So far, Meitu has ranked No.1 by daily new user and No.2 by weekly active penetration rate. From a global scope, it’s available in more than 26 countries and been downloaded more than 1 billion times. Meitu is a phenomenon.
Figure 3. Photo editing app–daily new user average. (Image source: www.199it.com)
Figure 4. Photo editing app–weekly active penetration rate. (Image source: cn.data.cmcm.com)
In order to get a general understanding of how it works, first we can take a quick tour on its interface and main features.
Click on the pink icon with characters “Meitu”, which stand for “beautiful picture”, and then we are on the main page that includes multiple options for photo editing. All of those modules are presented with understandable icons to imply their functions: a camera icon for taking photos, magic wand for basic editing, woman figure for reshaping portrait, grids for collage, a notebook for tips, etc.
Figure 5. Meitu chart-flow
Among all those modules on the main page, “Edit” and “Beautify” are two essential parts that aggregate core functions. “Edit” gathers a number of basic photo editing options that are relatively standards, such as “auto-enhance, crop”, “brightness”, “text”, “eraser”, etc. Whereas “Beautify” is to retouch on human face or body shape: enhancing skin, erasing acne and wrinkles, slimming down one’s face, making one look taller, etc. To operate those functions, all we need to do are dragging the bar down on the page or simply touching up on screen. Those core functions are basically the Photoshop sections but in a “one-touch” way. Another star feature in Meitu is ArtBot Andy, an AI robot that repaints selfies with a choice of styles and visual effects. At the time of writing a total of 712,042,886 users had witnessed the ArtBot’s “tech magic”.
Overall, the interface of Meitu is straightforward. Details about those functions mentioned above will be further discussed in the following part. Actually, Meitu did not invent any novel technologies throughout its development, but it is still one of the users’ favorites. What makes it possible? What makes it user-friendly? This article will answer those questions by re-examining Meitu app from a design perspective: combination, constraints, and sociotechnical system.
What makes Meitu possible?
- Technology combination
In the book “The Nature of Technology”, one core principle illustrating the essence of technology is combining existing elements, which are technologies themselves. Every novel technology, according to Arthur, was born as a hybrid of mechanical and organic. So was Meitu app.
When people say Meitu app is an innovator, they are talking about its innovation of presenting multifunction. As a commercial success in photo editing mobile application market, Meitu clearly knows the power of combining existing technologies. Throughout its development, Meitu itself comes with no new technological breakthroughs but adopts existing technologies to one platform where all of them come into play collaboratively.
Figure 6. Meitu neutral network inference framework. (Image source: mtlab.meitu.com)
- Image processing programs
“P图”, a phrase that means editing photos, were not that popular in China until the rise of Meitu. In this phrase, “P” originates from the name of a professional graphics editor, Photoshop, which was developed by Adobe systems. The first version of Adobe Photoshop was released in the 1980s when photo editing computer programs emerged. It does have magical features, with a variety of color adjustments, filter toning, highlight coverage, local processing, free transformation, etc., precisely modifying the picture. However, such a complex system requires a large amount of manual adjustments and is not something that every user can tackle.
As mobile devices become ubiquitous, various photo editing apps are emerging one after another. They can access the camera anytime by implementing the system API–application programming interface–and collect images from it for processing. Those magical features of photo apps are actually realized by integrated, stylized, template-based series of image processing programs.
Figure 7. MTenhance: Image Enhancement. (Image source: mtlab.meitu.com)
For removing the defects of an image such as spots and acne on the face, it is usually a matter of changing the color and greyscale of the skin around the face. If a part of the photo has a relatively large gray level, it will be detected as a “noise”. Therefore, the secret to clearing acne is actually the “noise reduction” in image processing. This is the most common way to handle images and includes various algorithms, such as the filtering algorithm, which aims to replace the original value with the average of surrounding gray levels for the purpose of lowering the gray level difference to make the noise not so obvious. Through the user interface, users were just removing the spots with a simple drag on the adjustment bar under this function where they are actually setting a threshold for the noise reduction.
Similarly, most popular filters are characterized by overexposure, low contrast, and offsets of shadow and highlight hue. In 2011, Instagram pioneered in the field of “automatic filter” through the combination of brightness, contrast, and saturation in one process.
Simply put, in terms of basic photo editing, Meitu is more like a light-weight version of Photoshop, with lower technical requirements. Although image editing software like Meitu seems to be more updated and more powerful, the basic image editing programs are already mature and are similar to those of the past.
- Face recognition
Another important feature in Meitu is reshaping one’s face and eyes and adding make-up or accessories automatically. This function is actually realized by the facial recognition system, which is capable of identifying or verifying a person from a digital image or video source. Facial recognition could be traced back to 60 years ago and has been used in various areas: security system, financial authentication, brand and PR agencies, etc. In the past decade being utilized in entertainment apps more frequently.
Figure 8. MT Face: Face-related technology. (Image source: mtlab.meitu.com)
The realization of face adjustment is clearly structured: face detection–key point positioning–region harmonizing–color blending–edge blending. Face detection is the capture of how many faces on a photo. Now it has overcome the problems caused by face angles, expression changes, light intensity and so forth. The basis of face detection is the key point positioning, that is, to find where the nose is, where the eyes are, and this process is often achieved by neural network technology for machine learning. After the key point positioning, the operation of “reshaping face” has a foundation. After finding the outline of the face, the shape can be changed through certain calculations and graphics transformations. The same is true for the eyes, eyebrows, and mouth. Then, in order to make sure that it can be coordinated with the picture to be implanted, it is necessary to color-harmonize the face to ensure its color consistency and image fusion. The last thing to do is to implant the face features into the prepared template.
Figure 9. Deep Neural Network. (Image source: www.ithome.com)
Nowadays, Meitu makes the adjustment process can be realized in a real-time manner–with the help of deep neural network technology. Turn on the front camera, and we can see a beautified self.
- Image segmentation and generation
Andy the ArtBot, labeled as the world’s first A.I. painting robot, is now a superstar in Meitu app. Meitu Inc has been researching and developing artificial intelligence for years. In 2010, Meitu Inc established the Meitu Imaging Laboratory (MTlab). In 2012, MTLab began to pay attention to artificial intelligence and deep learning. Andy is the latest outcome of MTlab—more specifically–its latest successful case of combining image segmentation and generation technology.
Figure 10. MTgenerate: Image generation. (Image source: mtlab.meitu.com)
Actually, those have previously been applied in the field of painting. For example, Google’s AutoDraw can match a user’s sketch with an existing image in the database to complete this picture. At the Davos Forum, Kai-Fu Lee also tried to use a robotic arm to do a painting. Comparing to image matching and robotic arm painting, completely repainting a portrait—as Andy does—is not something brand new but a bit more complicated.
By dismantling technologies used in Andy, we will find the following steps:
First, Andy learned a lot of illustrations, based on which he created a generic painting model. That is a long-term process of image generation. The core of image generation technology is based on the production network “Draw Net” that developed by MTlab. Draw Net is responsible for constructing painting models through big data analysis and deep learning. The artistic styles of those models all generate from a database that includes various compositions and strokes.
Second, after seeing the user’s selfie, with the help of facial recognition technology (which we have mentioned above), Andy grasps the contours and facial features of the user.
Then it locates the hair, clothes and background areas by using image segmentation technology.
Finally, by using the painting model to present the main features, Andy finishes his job.
It is not the first time that Meitu applies image generation and segmentation technology. At the beginning of 2017, Meitu app launched a “hand-drawn” feature, which is their initial try of combining facial and segmentation technologies. Also, prior to Andy’s birth, generation technology was being used in a fun feature in Meitu, from which users can see what they will look like if they were Europeans. Through big data and deep learning, the “machine” mastered the facial features of people from different countries and then uses Draw Net to generate a network, for the purpose of converting the user’s Asian features into European features. Andy, in this sense, is a mature form in this combinatorial revolution.
2. Software & hardware dependence
By borrowing existing technologies, the software is well prepared to be implemented. The next step is to find a “medium” to place it. Here, the medium mainly refers to two aspects: software and hardware.
As for software, it means the operating system (OS). Specifically, it is a collection of software that manages hardware and provides services for programs like Meitu. It is able to hide hardware complexity, manages computational resources, and provides isolation and protection. Meitu is no different with any other programs. To use Meitu on laptops or smartphones, people don’t have to literally speak binary, comprehend machine code of this program but understand it in a streamlined graphical user interface (GUI). Through this interface we can work on the image with a mouse or a finger, clicking and seeing them happening right in front of you. All this translation work is done by the translator in your device—the operating system. Most of us are using them every day: Windows, Mac, Linux, Android, iOS, etc. There are universal key elements of those operating systems. The first one is abstraction. They basically eliminate all the unnecessary, redundant “things”: process, thread, file, sockets, memory, etc. The second one is mechanism. Main actions it conducts include creating, scheduling, opening, writing and allocating.
Hardware, as its name implies, includes tangible components of a computer: motherboard, central processing unit, memory, storage, monitor, mouse, keyboard, etc. for the personal computer; display, camera, application processor, sensor, memory, etc. for mobile devices. Although they seem to have nothing to do with Meitu itself, still, software and hardware are two prerequisites for Meitu and other applications.
The secret behind the large user base of Meitu partly lies in its low requirements of hardware and software dependence. In 2008, Meitu was initially born as a computer photo editing software. Unlike Photoshop, which is relatively high demanding in hardware requirements especially processor, RAM, hard-disk space, etc., Meitu is much more light-weight. Plus, compared to its mobile application competitors, there are multiple choices of operating systems that are compatible with Meitu app. The applicable operating systems so far include Android, iOS, Windows, iPad, WindowsPhone.
“Meiyan camera”, all also called beauty camera, is a trendy function in Meitu app by which users will get auto-beautified selfies in real time. The operating process of “Meiyan” camera, could be a good case of wrapping up all the combinatorial components in Meitu app.
Figure 11. Flowchart: how beauty camera works
When users turn on Beauty camera and start taking photos, Meitu would get connected to their camera and the ambient light sensor embedded in the smartphone, detecting the surrounding environment. If the light is too dark and causes much noise, the application will automatically turn on the noise reduction/image denoising and exposure correction to make sure the result is noise-free and bright. Meanwhile, for the purpose of making further adjustment like enlarging eyes, smoothening skin, etc., and realizing beautification function, the app turns on its facial recognition to grasp main physical features in picture and then applying other image editing techniques in order to get a facial beautification effect (like smoothening skin, enlarging eyes, etc.). Thanks to the synergy and combinatorial property of Meitu, the whole process of taking photos and retouch runs smoothly within three seconds.
What makes Meitu usable?
As a platform gathering various powerful technologies, another important responsibility for Meitu app is to increase the usability of the application, which means to hide the complex behind technical part, reduce the possibilities of misleading or jeopardizing efficiency. To realize that, application designers need to make the interface clear and intuitive. Here, the concept of “constraints” has to be taken into consideration.
According to Donald Norman´s classic The Design of Everything Thing, constraints is to limit the actions of users on a system. By restricting users’ behavior, designers can help users understand the status of the system they are in and thus reduce the chance of errors. Through the interface of Meitu app, we could easily find how constraints are applied.
Paths are to help users control in a limited variable range. Usually, they are designed in forms of progress bar or channels, of which shape restrict users’ action to linear motion. Most of image editing functions in Meitu embed intensity bars for the user to make adjustment intuitively. The interface is neat and clean, with only a linear bar on it. As it shows, hardly could users misuse this mode.
Figure 12. Screenshot: brightness intensity bar
Barriers are designed for redirecting users’ actions, which are heading to a relatively negative or unsuccessful result. If users press the backward button in the middle of the image editing process, the dialogue box will pop out asking the user if he/she decides to quit or not. In another case when image quality would potentially be harmed after editing, the system will inform users with an attention box. With barriers applied to the interface, users are given more transparency to what consequences they are going to face and more agency to make a choice.
Figure 13. Screenshot: barrier settings in Meitu
In terms of design, symbols take the form of text, sound, visual images, etc., which are used for categorizing, clarifying, and cautioning users about certain actions. “Undo/Redo” options at the top of the screen would be a good example of symbols being used for constraint in Meitu. When the user cannot undo/redo previous effects any further, the “back” or “forward” symbol would be grayed out as a caution. Similarly, a direct text notification can be viewed as a constraint if the system is unable to complete a certain action.
Figure 14. Screenshot: symbol setting in Meitu
Conversational interfaces are of great necessity because through which users are given the opportunity to speak to their devices. Otherwise, the interface will function inefficiently considering the endless possibilities of how to use it will be frustrating to the users. From this perspective, Meitu is qualified for implementing multiple forms of constraints.
How Meitu exerts influence on society?
“If someone shoots another with a gun, who is doing the shooting—is the person or the gun?”, Latour asked. The seemingly absurd question highlights the necessity to think about the relationship between humans and non-human artefacts. From a sociological perspective, humans and technical objects cannot be separate but intermingle. Latour makes this intertwining clear on a conceptual level by introducing “technical mediation.” The gun is a mediator that actively contribute to the way in which the end is realized. The same is also true for Meitu app. Under a sociotechnical context, we could never ignore its interaction with other components and how they influence one another.
- Beauty Obsession
There is a name for a new kind of face perfected by the Meitu app–with enlarged eyes, sharpened chins, pale skin–which now you see everywhere on the internet and even reality: “Wang hong lian” (internet celebrity face). This trend is fueled by the centuries-old tradition obsession with flawless skin and big eyes. On the one hand, Meitu leads this trend and continuously consolidates public views towards the concept of beauty with its technical tricks embedded. On the other, such an epidemic of stereotype counter-forces Meitu to constantly upgrade its popular functions related to face reshaping.
- Culture penetration
During the 2018 spring festival, Meitu launched a new activity to celebrate, called “winning gift money with face score”. By using its AI ArtBot function, users send a selfie portrait to the system to calculate the user’s facial attractiveness and then they get gift money according to the rating. Debuted on 15 February, the activity attracted many users, and two million yuan for the Spring Festival was claimed very rapidly. Apparently, Meitu added more fun to a traditional custom with the help of technology.
- Commercial pressure
Under the guidance of mass culture, Meitu is undoubtedly transforming from industrial products to consumer products. The emergence of social media enables it a higher degree of generality and greater penetration. By frequent sharing of selfies retouched by Meitu on social media, young people unintentionally help providers to promote their products and services. and therefore contribute to the commercial pressure.
In ten year, Meitu has been installed on more than one billion phones mostly in Asia areas. It has been estimated that more than half the selfies uploaded on Chinese social media have been retouched by Meitu. Apparently, its popularity is the result of synergies between different actors and organizations, meaning that we users and application itself are co-evolving constantly and mutually. From the very birth of any software application, it was being influenced by users’ needs, market conditions, technology development, etc. In the case of Meitu app, mobile devices development, software technology, target costumer’s behavior (mainly referring to female under 30), current social background directly decide how the app would be designed and updated. On the other hand, Meitu is also changing–or becoming–a part of Chinese culture.
As the pioneer photo editing app in China, Meitu app is innovative in combining and transforming multiple existing technologies on one platform, shortening the distance between users and emerging technologies within an intuitive interface. Before selfie apps were everywhere, Meitu set a basic model for its followers, explored various possibilities for the future trend of photo-editing: face-related technologies, image generation, motion capture…it is fair to say the biggest achievement of Meitu is introducing cutting-edge technologies to everyday life, with much fun.
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