Self-directed ongoing project (2019)
Images take up the significant part of online information but perceiving pictures are challenging for people with low vision. Through this project, I explore image enhancement technology as an alternate means to aid people with low vision.
According to World Health Organization, about 188.5 million people have mild to moderate visual impairment. The term "low vision" refers to a non correctable visual impairment through surgery, pharmaceuticals, glasses, or contact lenses. Vision loss is a complex disability. There is no standard describing low vision because each individuals have very different level of visual acuity. Therefore, many users still experience some level of difficulty in perceiving images with the existing vision aid tools.
Above chart shows common accessibility options used for perceiving images. Except for image-to-text, these tools modify attributes of image. However, they only provide a simple image modification which only is effective for certain types of visual impairment. These are some of my research questions.
“Do current low vision accessibility tools provide enough support to diverse user groups?”
“What are the unsolved problems when perceiving the image contents
using accessibility tools?”
“How image enhancement technology can help people with low vision
to have better perceive image-based contents?
I built a Chrome extension which allows users to build their own photo filters and apply to images shown on the web browser.
During the user interview, I collected information about what vision aid tools are used and what strategy they use when they fail to perceive images with those tools.
1. People with low vision rely on additional software, hardware, to cover the disparity that accessibility tools cannot be help of. In the case of using additional software, they often encounter with difficulties in using it, as it is not designed to support people with visual impairment.
2. Modifying the hardware setup is another option, but takes too much time.
e.g. adjusting monitor settings every time they load the image.
3. In the case of requesting help to their family, friends and peers, they are not always available to assist them.
What affects the perceived visibility?
From both academic papers and user interviews, I found a strong evidence that sharpness and contrast are the two major variables affecting the perceived visibility of images. For example, Leat’s scholarly article shows that moderately sharpened images with enhanced contrast has the highest visibility to people with low vision.
Prototype V1: testing statistical results.
I decided to create an add-on to test out my findings. During this stage, I focus on verifying the usefulness of theoretical results with real-world application. Here is a main reason why I decided to build a solution as a chrome extension. Majority of people with low vision prefers laptop than mobile, due to its large screen size. Therefore, a desktop application would be practical than a mobile version.
This is a finalized interface. The page has an app title, a short description, contrast and sharpness sliders and a button to switch themes.
Here you can see how the prototype works with the web browser.
I ran a usability testing with a focus group. Each session was about an hour, it consists 40 minutes of observation and 20 minutes of post interview. These were the main questions I investigated during usability testing:
How does the app improve perceived visibility on the images for users?
How well does the app integrate with the user’s internet experience?
Are there any additional features that can help users perceive images better?
Here are some notes from the interview:
Majority of interviewees responded that the app helped them identify images quicker and more clear.
Respondents suggest the feature to display the image on a separate tab so that they can concentrate better on the image itself.
Some respondents also mentioned the instruction text is too small to read comfortably.
The most interesting feedback I received was that the app decreases the readability of the text in the image.
Prototype V2: implementing user feedback
Based on the feedback collected from the testing, I made some changes:
More customization options.
Providing an ideal solution for each individual with limited image customization is challenging. I provide more control on image filter to fulfill more diverse user needs.
Displaying image on a separate tab.
I learned that image resolution is as important as sharpness and contrast in perceived visibility. Usually accessing the full-size image requires multiple mouse-click, and this is a big problem for some people with low vision. in prototype V2, I made the app to display the full-size image on a separate tab, and apply the filter immediately with a single click.
Increased readability of UI.
For people with low vision, securing am optimal readability is an important goal. I increased font size, added an eye icon to display app status(on/off) and used primary colors to make UI more visible.
Additional features to assist users.
Besides the image filter, it has features automatically display the image in full-screen mode, adjust background color to maintain ideal contrast value, and detect text in the image.
I ran another usability testing with different users. According to the survey, there was a noticeable improvement in usability rating. The average rating of the app increased to 4.0! The minimum rating and the maximum rating goes up to 2.5 and 5 respectively.
Future project direction
In social media spaces, sharing a snapshot of pictures is being a popular way of communicating with their family and friends. However, things like post length, styled text, and AR filter has low visibility for people with visual impairment. I am currently exploring how text detection and image enhancement technology can help in tackling these problems.