Eve A. Riskin

MobileASL:: intelligibility of sign language video as constrained by mobile phone technology

For Deaf people, access to the mobile telephone network in the United States is currently limited to text messaging, forcing communication in English as opposed to American Sign Language (ASL), the preferred language. Because ASL is a visual language, mobile video phones have the potential to give Deaf people access to real-time mobile communication in their preferred language. However, even todays best video compression techniques can not yield intelligible ASL at limited cell phone network bandwidths. Motivated by this constraint, we conducted one focus group and one user study with members of the Deaf Community to determine the intelligibility effects of video compression techniques that exploit the visual nature of sign language. Inspired by eyetracking results that show high resolution foveal vision is maintained around the face, we studied region-of-interest encodings (where the face is encoded at higher quality) as well as reduced frame rates (where fewer, better quality, frames are displayed every second). At all bit rates studied here, participants preferred moderate quality increases in the face region, sacrificing quality in other regions. They also preferred slightly lower frame rates because they yield better quality frames for a fixed bit rate. These results show promise for realtime access to the current cell phone network through signlanguage-specific encoding techniques.

Distortion-Complexity Optimization of the H.264/MPEG-4 AVC Encoder using the GBFOS Algorithm

The H.264/ACV standard provides significant improvements in performance over earlier video coding standards at the cost of increased complexity. Our challenge is to determine H.264 parameter settings that have low complexity but still offer high video quality. In this paper, we propose two fast algorithms for finding the H.264 parameter settings that take about 1% and 8%, respectively, of the number of tests required by an exhaustive search. Both the fast algorithms result in a maximum decrease in peak-signal-to-noise ratio of less than 0.71 dB for different data sets and bitrates

Variable frame rate for low power mobile sign language communication

The MobileASL project aims to increase accessibility by enabling Deaf people to communicate over video cell phones in their native language, American Sign Language (ASL). Real-time video over cell phones can be a computationally intensive task that quickly drains the battery, rendering the cell phone useless. Properties of conversational sign language allow us to save power and bits: namely, lower frame rates are possible when one person is not signing due to turn-taking, and signing can potentially employ a lower frame rate than fingerspelling. We conduct a user study with native signers to examine the intelligibility of varying the frame rate based on activity in the video. We then describe several methods for automatically determining the activity of signing or not signing from the video stream in real-time. Our results show that varying the frame rate during turn-taking is a good way to save power without sacrificing intelligibility, and that automatic activity analysis is feasible.

H.264/MPEG-4 AVC Encoder Parameter Selection Algorithms for Complexity Distortion Tradeoff

The H.264 encoder has input parameters that determine the bit rate and distortion of the compressed video and the encoding complexity. A set of encoder parameters is referred to as a parameter setting. We previously proposed two offline algorithms for choosing H.264 encoder parameter settings that have distortion-complexity performance close to the parameter settings obtained from an exhaustive search, but take significantly fewer encodings. However they generate only a few parameter settings. If there is no available parameter settings for a given encode time, the encoder will need to use a lower complexity parameter setting resulting in a decrease in peak-signal-to-noise-ratio (PSNR). In this paper, we propose two algorithms for finding additional parameter settings over our previous algorithm and show that they improve the PSNR by up to 0.71 dB and 0.43 dB, respectively. We test both our algorithms on Linux and PocketPC platforms.

MobileASL: Intelligibility of sign language video over mobile phones

For Deaf people, access to the mobile telephone network in the United States is currently limited to text messaging, forcing communication in English as opposed to American Sign Language (ASL), the preferred language. Because ASL is a visual language, mobile video phones have the potential to give Deaf people access to real-time mobile communication in their preferred language. However, even todays best video compression techniques can not yield intelligible ASL at limited cell phone network bandwidths. Motivated by this constraint, we conducted one focus group and two user studies with members of the Deaf Community to determine the intelligibility effects of video compression techniques that exploit the visual nature of sign language. Inspired by eye tracking results that show high resolution foveal vision is maintained around the face, we studied region-of-interest encodings (where the face is encoded at higher quality) as well as reduced frame rates (where fewer, better quality, frames are displayed every second). At all bit rates studied here, participants preferred moderate quality increases in the face region, sacrificing quality in other regions. They also preferred slightly lower frame rates because they yield better quality frames for a fixed bit rate. The limited processing power of cell phones is a serious concern because a real-time video encoder and decoder will be needed. Choosing less complex settings for the encoder can reduce encoding time, but will affect video quality. We studied the intelligibility effects of this tradeoff and found that we can significantly speed up encoding time without severely affecting intelligibility. These results show promise for real-time access to the current low-bandwidth cell phone network through sign-language-specific encoding techniques.

Activity analysis enabling real-time video communication on mobile phones for deaf users

We describe our system called MobileASL for real-time video communication on the current U.S. mobile phone network. The goal of MobileASL is to enable Deaf people to communicate with Sign Language over mobile phones by compressing and transmitting sign language video in real-time on an off-the-shelf mobile phone, which has a weak processor, uses limited bandwidth, and has little battery capacity. We develop several H.264-compliant algorithms to save system resources while maintaining ASL intelligibility by focusing on the important segments of the video. We employ a dynamic skin-based region-of-interest (ROI) that encodes the skin at higher quality at the expense of the rest of the video. We also automatically recognize periods of signing versus not signing and raise and lower the frame rate accordingly, a technique we call variable frame rate (VFR).n We show that our variable frame rate technique results in a 47% gain in battery life on the phone, corresponding to an extra 68 minutes of talk time. We also evaluate our system in a user study. Participants fluent in ASL engage in unconstrained conversations over mobile phones in a laboratory setting. We find that the ROI increases intelligibility and decreases guessing. VFR increases the need for signs to be repeated and the number of conversational breakdowns, but does not affect the users perception of adopting the technology. These results show that our sign language sensitive algorithms can save considerable resources without sacrificing intelligibility.

A web-based user survey for evaluating power saving strategies for deaf users of mobileASL

MobileASL is a video compression project for two-way, real-time video communication on cell phones, allowing Deaf people to communicate in the language most accessible to them, American Sign Language. Unfortunately, running MobileASL quickly depletes a full battery charge in a few hours. Previous work on MobileASL investigated a method called variable frame rate (VFR) to increase the battery duration. We expand on this previous work by creating two new power saving algorithms, variable spatial resolution (VSR), and the application of both VFR and VSR. These algorithms extend the battery life by altering the temporal and/or spatial resolutions of video transmitted on MobileASL. We found that implementing only VFR extended the battery life from 284 minutes to 307 minutes; implementing only VSR extended the battery life to 306 minutes, and implementing both VFR and VSR extended the battery life to 315 minutes. We evaluated all three algorithms by creating a linguistically accessible online survey to investigate Deaf peoples perceptions of video quality when these algorithms were applied. In our survey results, we found that VFR produces perceived video choppiness and VSR produces perceived video blurriness; however, a surprising finding was that when both VFR and VSR are used together, they largely ameliorate the choppiness and blurriness perceived, i.e., they each improve the use of the other. This is a useful finding because using VFR and VSR together saves the most battery life.

Activity detection in conversational sign language video for mobile telecommunication

The goal of the MobileASL project is to increase accessibility by making the mobile telecommunications network available to the signing Deaf community. Video cell phones enable Deaf users to communicate in their native language, American Sign Language (ASL). However, encoding and transmission of real-time video over cell phones is a power-intensive task that can quickly drain the battery.

Enabling access through real-time sign language communication over cell phones

The primary challenge to enabling real-time twoway video conferencing on a cell phone is overcoming the limited bandwidth, computation and power. The goal of the MobileASL project is to enable access for people who use American Sign Language (ASL) to an off-the-shelf mobile phone through the implementation of real-time mobile video communication. The enhancement of processor, bandwidth, and power efficiency is investigated through SIMD optimization; region-of-interest encoding based on skin detection; video resolution selection (used to determine the best trade off between frame rate and spatial resolution); and variable frame rates based on activity recognition. Our prototype system is able to compress, transmit, and decode 12–15 frames per second in real-time and produce intelligible ASL at 30 kbps. Furthermore, we can achieve up to 23 extra minutes of talk time, or a 8% gain over the battery life of the phone, through our frame dropping technique.

Evaluating quality and comprehension of real-time sign language video on mobile phones

Video and image quality are often objectively measured using peak signal-to-noise ratio (PSNR), but for sign language video, human comprehension is most important. Yet the relationship of human comprehension to PSNR has not been studied. In this survey, we determine how well PSNR matches human comprehension of sign language video. We use very low bitrates (10-60 kbps) and two low spatial resolutions (192×144 and 320×240 pixels) which may be typical of video transmission on mobile phones using 3G networks. In a national online video-based user survey of 103 respondents, we found that respondents preferred the 320×240 spatial resolution transmitted at 20 kbps and higher; this does not match what PSNR results would predict. However, when comparing perceived ease/difficulty of comprehension, we found that responses did correlate well with measured PSNR. This suggests that PSNR may not be suitable for representing subjective video quality, but can be reliable as a measure for comprehensibility of American Sign Language (ASL) video. These findings are applied to our experimental mobile phone application, MobileASL, which enables real-time sign language communication for Deaf users at low bandwidths over the U.S. 3G cellular network.