In the above paper (ibid., vol. 22, no. 12, pp. 5146-5157, Dec. 2013), a delay in communicating proof errors occurred. The paper was published without corrections. The corrections presented here are important.

The development of video quality metrics requires methods for measuring perceived video quality. Most of these metrics are designed and tested using databases of images degraded by compression and scored using opinion ratings. We studied video quality preferences for enhanced images of normally-sighted participants using the method of paired comparisons with a thorough statistical analysis. Participants (n=40) made pair-wise comparisons of high definition video clips enhanced at four different levels using a commercially available enhancement device. Perceptual scales were computed with binary logistic regression to estimate preferences for each level and to provide statistical inference of the differences among levels and the impact of other variables. While moderate preference for enhanced videos was found, two unexpected effects were also uncovered: 1) participants could be broadly classified into two groups: a) those who preferred enhancement ("Sharp") and b) those who disliked enhancement ("Smooth") and 2) enhancement preferences depended on video content, particularly for human faces to be enhanced less. The results suggest that algorithms to evaluate image quality (at least for enhancement) may need to be adjusted or applied differentially based on video content and viewer preferences. The possible impact of similar effects on image quality of compressed video needs to be evaluated.

Virtual environments (VEs) allow safe, repeatable, and controlled evaluations of obstacle avoidance and navigation performance of people with visual impairments using visual aids. Proper simulation of mobility in a VE requires an interface, which allows subjects to set their walking pace. Using conventional treadmills, the subject can change their walking speed by pushing the tread with their feet, while leveraging handrails or ropes (self-propelled mode). We developed a feedback-controlled locomotion interface that allows the VE workstation to control the speed of the treadmill, based on the position of the user. The position and speed information is also used to implement automated safety measures, so that the treadmill can be halted in case of erratic behavior. We compared the feedback-controlled mode to the self-propelled mode by using speed-matching tasks (follow a moving object or match the speed of an independently moving scene) to measure the efficacy of each mode in maintaining constant subject position, subject control of the treadmill, and subject pulse rates. Additionally, we measured the perception of speed in the VE on each mode. The feedback-controlled mode required less physical exertion than self-propelled. The average position of subjects on the feedback-controlled treadmill was always within a centimeter of the desired position. There was a smaller standard deviation in subject position when using the self-propelled mode than when using the feedback-controlled mode, but the difference averaged less than six centimeters across all subjects walking at a constant speed. Although all subjects underestimated the speed of an independently moving scene at higher speeds, their estimates were more accurate when using the feedback-controlled treadmill than the self-propelled.