Evaluating Barten image metric for predicting character recognition in people with low vision.

INTRODUCTION In today's high-tech environment, the use of electronic devices with small visual displays (SVDs) is an everyday occurrence. The potential use of devices with SVDs is present in all eight areas of occupation identified by the American Occupational Therapy Association [1]: personal activities of daily living (blood glucose monitors), instrumental activities of daily living (kitchen appliances), rest and sleep (alarm clocks), education (e-readers and tablet computers), work (fax and copy machines), play (video games), leisure (MP3 and DVD players), and social participation (cellular telephones). In developed countries, rarely does someone go through a day without interacting with these types of devices. Use of these electronic devices with SVDs can be difficult to impossible, however, for the millions of Americans with low vision associated with eye diseases, neurological conditions, and aging. Some devices have been adapted to be easier to see or to be used with alternative senses. Smartphones and tablet computers usually have built-in magnification software, MP3 players often have speech output, and "talking" glucometers and blood pressure monitors exist. Even these adapted devices have functional limitations [2], however, and the majority of electronics with SVDs have no accessibility options. This presents a barrier to participation in meaningful roles and activities for people with low vision. It also places a financial burden on the individual, the family, and society when people can no longer independently manage their medications, finances, or home management tasks. This increased need for assistance can lead to excess disability, overprotection, caregiver burden, and negative interpersonal relationships within social networks [3-7]. It is crucial that people with visual impairments be given improved access to SVDs on electronic devices. There are two levels at which this can be accomplished. The first method requires manufacturers to create devices using principles of universal design with displays having optimum contrast, reflection, and font size. Rather than taking these ideal visual characteristics into account, manufacturers currently base their practices on inexpensive and readily available liquid crystal display (LCD) technology [8-10]. Providing specific guidelines about which characteristics enhance usability of displays could assist manufacturers in producing devices that are accessible to the millions of people with low vision. The second method for increased access is improvements in vision rehabilitation treatments and technology. A more thorough understanding of how individuals with low vision interact with and use SVD devices could inform rehabilitation professionals' decision-making processes as they work with clients to utilize ideal magnifiers, lighting, visual skills, and adaptive techniques. One potential tool to assist manufacturers in designing more accessible SVDs and to help rehabilitation professionals choose techniques and visual aids that will allow clients to access SVDs is the Barten square root integral (SQRI) image quality metric. The Barten SQRI is an image metric that was originally developed to measure whether people with normal vision could see images on cathode ray tube (CRT) televisions and computer monitors. One component of the Barten SQRI, the modulation transfer function (MTF), describes how an image-forming system (display) filters the modulation (contrast) as a function of spatial frequency [11]. In other words, the MTF is a measure of how an electronic device transmits the contrast levels of the images it produces on a display. However, it is difficult to measure the perceived quality of an image because it depends on both the physical parameters (e.g., MTF) of the system as well as how the visual system (eye and brain) perceives the image. An image quality metric--a mathematical expression that incorporates the physical parameters of the image produced by the system with the characteristics of the human observer--can be used to measure the perceived quality of an image. …