The Digital Transformation of Visual Media: 5 Key Developments

I was recently looking at old syllabuses from my introductory graduate course on visual communication, and I noticed an interesting trend: the rate of change from one year’s syllabus to the next one’s has been much more rapid in recent years than in the past. The major reason for this trend is that visual media themselves have been changing so rapidly in recent years as a result of digitization. Moreover, to a considerable extent these changes have entailed genuine innovations in the forms and functions of visual media. In other words, while some developments in digital media are mainly concerned with doing a better job in tasks that older, analog media were not very good at (for example, 3D movies), digitization has also enabled visual media to do some things that analog media couldn’t do at all.

What are the most significant developments in the digital transformation of visual media? Opinions undoubtedly differ, depending partly on one’s time-frame. In the 1990s, when Photoshop was still new, the manipulation of photographic truth was a major focus of research in visual communication. Today, photo-manipulation software is a mature technology, and, while the concerns it gave rise to have by no means gone away, the most eye-catching changes in the visual media landscape seem to be happening elsewhere.

Digital Composite WITH GAPS 12 percent

From my perspective, there are at least five big developments whose impact is still very uncertain – and, therefore, very deserving of closer scrutiny. In my view, these five developments will need to figure very prominently in the future work of visual communication researchers if our field is to keep up with the explosively rapid evolution of digital media.

For more than a decade, digital animators have been working towards the attainment of two major milestones in the development of visual media: first, the achievement of “perfect photorealism” – i.e., the ability to mimic not just the momentary appearance of visual reality (as in present-day digital images) but also its appearance over time (without relying on motion capture, which is essentially a relic of traditional cinematography); second, the ability to simulate the appearance (over time) of actual people, such as deceased actors. These developments have received some attention from media scholars, but it may be fair to say that, for now, the most promising lines of inquiry actually come from outside of communication, in studies of people’s responses to humanoid robots and visual displays. Research on the much-discussed but little-understood “uncanny valley” phenomenon is a good example of this area of scholarship


As computers and, hence, digital media have become cheaper, aspects of image creation that previously required substantial resources have become increasingly affordable and accessible, resulting in a democratization of visual production. This development is evident not only in the ubiquity of photographic and video devices, but also in the increasing ease with which photographs and video can be manipulated. (The emergence of consumer-level editing software in the late 1990s was a particularly noteworthy innovation for anyone who had previously had the extremely cumbersome experience of editing in celluloid-based film.) While there is a growing body of good scholarly writing about the dissemination of nonprofessional images (which I will refer to further in the next paragraph), there is surprisingly little systematic research on everyday people’s manipulation of images.

As I have implied above, I think it is useful to distinguish the democratization of visual distribution, for which, of course, we can thank the Web, from the democratization of production. The radical transformation in the distribution of new visual media is one of two items (out of my list of five topics) that have already been written about quite widely by media scholars. There are several books about YouTube, and a growing number of studies of Instagram, Snapchat, Vine, and other platforms.

The fourth item on my list (although, chronologically, it is actually the first) has to do with the emergence of videogames as a major visual medium. This phenomenon can be seen as part of a broader evolution in audiovisual technology, allowing the users of that technology (gamers, etc.) to project a controllable visual avatar of themselves into a virtual environment. In contrast to the previous two items on my list, the emergence of this type of avatar represents a qualitative (i.e, not just quantitative) break with the past. There is nothing in previous visual media that can give the user the same experience of an externally viewable but internally controlled projection of one’s self. Research on the visual aspects of avatars suggests that they can have profound implications. For example, they can have enduring effects on gamers’ or VR users’ aggressiveness, sociality, and attitudes towards people who are demographically different from their real selves.

The final item on my list is also related to games and other media that provide their users with the experience of interactive virtual environments. The ability to affect a virtual environment through one’s own actions represents another significant break with the past history of visual media. The purposive use of virtual environments as means of low-consequence training for high-consequence real-world activities (e.g., flight simulation, practice surgery) has been studied quite extensively by researchers from a wide variety of disciplines, including communication. Moreover, communications researchers have also devoted considerable attention to the more nebulous cultural consequences that may flow from people’s experiences in virtual worlds. All the same, I think it’s safe to say that to date we have barely glimpsed what lies ahead in this area of visual media. Perhaps more than in any of the other areas on this list, the technology and the social practices associated with this area seem to be changing more rapidly than our projections. PM

Why the Overwatch Trailer Calls for a New Type of Shooter Game

[embedplusvideo height=”283″ width=”450″ editlink=”” standard=”″ vars=”ytid=FqnKB22pOC0&width=450&height=283&start=&stop=&rs=w&hd=1&autoplay=0&react=1&chapters=&notes=” id=”ep6981″ /]

Blizzard Entertainment has a new debut: Overwatch, a Pixaresque Windows shooter game with a rumored 2015 beta release. Blizzard, known for its MMORPG World of Warcraft, has taken an exciting step in the development of this game.

The trailer opens with a Ken Burns Generalization Montage – an effect that combines still images with slight camera movement to create a feeling of ‘everyone together’. This montage combines a variety of push in and pull out movements in addition to multiple heroic angles (angles shot from below or above the subject to generate a sense of boldness) to create intensity. This opening, revealed to be a documentary lionizing the past, transitions into a wide shot of two neotenous boys. Neoteny, an effect most prominent in Japanese anime, shows bigger eyes and puffier cheeks for more child like faces. It’s generally appealing to audiences especially when audiences know the subjects aren’t real. This softer and gentler approach to a shooter game is a bit novel in a field crowded with games like Call of Duty. Beautifully animated characters fighting in steadily edited sequences exhibit that this game entails a potentially more casual gaming experience. Experienced gamers may find this gameplay a bit slow since their manual skills are highly developed (gamers learn skills like spatial recognition and response time); however, there is no halt to the excitement as the trailer reveals. Digital animation grants existence to unrealistic things in a realistic environment, and Overwatch epitomizes a shooter game mentality within kid-friendly environments.

3D Visual Illusion and Game

My final project was about 3D visual illusion and its application in game. I made a concept game using Unity3D.

I included description of the project in the video.


Merry Christmas to everyone.

Good luck!

BTW, I just wonder how can I post video. I once did that but then it disappeared.


Battle Simulation Experiment



For my final project for COMM 562, I conducted an experiment in developing a battle simulation system. The inspiration for this project came from reading and learning about software such as MASSIVE, an incredible scalable system for crowd simulation which is used to generate large crowd and battle scenes in movies such as Lord of the Rings, Inception, and Avatar. As movies and games continue to create scenes and environments with larger numbers of characters, it is increasingly more difficult to animate and control all of them by hand. Because of this, using a system that can dynamically control the units through algorithms and still produce realistic behavior is necessary, and is rapidly becoming a popular technique. In my project, I wanted to try and recreate such a system on a smaller scale, and experiment with how many units I could simulate.


For my project, I modified an existing framework for behavior animations to simulate basic behaviors from a battle environment. The characters are split into two groups, and each individual unit contains an algorithm that specifies certain features. Each unit will exhibit behavior similar to those seen in a battle. For example, if a unit is being attacked and is not currently attacking another unit, it will turn to attack its opponent. Also, if a unit is low on health, it will run away from its attacker and the battle in an escape attempt, returning momentarily in pursuit of a different opponent. In many instances where there are a large number of units present in the simulation, a unit may not be able to physically reach its target in order to attack. Therefore, it will choose another target closer to its location to begin attacking and not remain idle.


All of these behaviors combine to give some emergent patterns that are also realistic. One behavior I noticed was the tendency for units to gang up when attacking an opponent. Another was units flanking around the sides of a group in order to attack enemies in the back of the group. The initial charge and attack also simulates reality, where the front line takes the most damage and casualties. As the battle continues, the groups slowly disperse and integrate.


I conducted multiple different tests with different units to see how large of a simulation I could run. I initially expected to run a simulation with a few hundred units, because the code is not incredibly complicated. However, when I had more than 50 units on screen at once the simulation began to visibly lag. I expect this could be from the complexity of the character models and their associated animations. Although this limitation was a slight disappointment, I am still relatively pleased with the results.


I have compiled a video showing a battle between two, 18, and 50 units, which can be found here. Note that when a unit’s health reaches zero, it freezes and can no longer take action. The animation also immediately pauses, hence the units scattered all over the field in various state of attack when the battle ends.


The behavior code was written in C++, the character scripts in C#, and the environment constructed and simulated using the Unity game engine.

Jian Feng & Qing Sun: Visual Illusions

This is a joint project by Jian Feng and Qing Sun.

We carried out a survey about visual illusions based on the game we are making. We compiled our work to a voice video. Below is the link to the video.

We also attached the script for the video as a reference.

Thank you very much!


Visual Illusions

Hello this is Jian Feng and Qing Sun.

Out final project is about “visual illusions” in games, especially illusions on smoothness and speed.

For this purpose we created ten video clips from the game we are making. The game is called “STEER ‘n’ SLIDE”, in which the player is sliding in an endless tunnel. Based on the video clips we carried out a survey to see how it delivers an illusion of smoothness and speed. For all of the control experiments, the tunnel is moving at the same speed, and the tunnel’s geometries and meshes are exactly the same. The only thing we modified is the tunnel’s texture in each of the ten video clips.

Up to now 63 people have taken the survey and below shows the survey results.

In the first three videos we do survey about how to create a smooth and convenient illusion.
1. We add a triangle at each of the four corners of the texture, which cover the sharp angle between the mesh polygons. This works well to create a smoother illusion with the same polygonal geometry.

2. Sometimes when the avatar or the environment is spinning, the player may feel dizzy. This test shows that concentrating on objects far away could help reduce the dizzy feeling.

3. We also want to know if smoothness helps. Apparently, for most people, the smoothed round tunnel feels much better than the polygonal tunnel.

In the other videos we focus on the sense of speed.
4. In this video clip the parallel lines in the right tunnel are stronger and darker than those in the left one, which actually decrease the sense of speed according to the result. From this we assume that the perpendicular lines are more significant to generate the sense of speed, and since the stronger parallel lines relatively weaken the visual effect of the perpendicular lines, the right tunnel seems to move slower. We prove our assumption in the next few clips.

5. So in the fifth clip we strengthened the perpendicular lines instead, and the result shows that more people agree that the one with stronger perpendicular lines is moving faster. However, there are still 32% of people thinking in the other way. So this might not be the most important factor.

6. Then what about density? We double the density of the parallel lines. Yeah, more people think the tunnel with more parallel lines moves faster. But again, we could only cheat 43% people’s eyes.

7. Well, if we increase the density of the perpendicular lines, nearly all of the responses agree it is faster. So with more perpendicular references, it could easily make us feel we are moving faster than we actually do.

8. Are there other factors? We attached a relatively realistic texture to the meshes. To our disappointment, people still think the simpler tunnel moves faster. We found out this is because the simpler texture has a relatively blurry effect, which may help increase the sense of speed.

9. So we added a blurry effect to the same texture and it works! Most people agree that the blurred one is at higher speed. In addition, because the left tunnel, which should be without any blur, is still moving with some “resolution loss” blurry effect since we uploaded it to a website(Vimeo), we believe that the difference would be much apparent if the tester watched the original video.

10. And, of course, we still remember that we were shown two car racing videos where the cameras were located above or at the bottom of the car at class. So we also carried out a survey about the camera’s location. From the responses, apparently, the lower the camera is, the faster we feel.

Conclusion and Examples
<span style=%

Copyright © 2017 visualinquiry