TY - GEN
T1 - Watch It, Don't Imagine It
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
AU - Amin, Akhter Al
AU - Hassan, Saad
AU - Lee, Sooyeon
AU - Huenerfauth, Matt
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - Television captions blocking visual information causes dissatisfaction among Deaf and Hard of Hearing (DHH) viewers, yet existing caption evaluation metrics do not consider occlusion. To create such a metric, DHH participants in a recent study imagined how bad it would be if captions blocked various on-screen text or visual content. To gather more ecologically valid data for creating an improved metric, we asked 24 DHH participants to give subjective judgments of caption quality after actually watching videos, and a regression analysis revealed which on-screen contents' occlusion related to users' judgments. For several video genres, a metric based on our new dataset out-performed the prior state-of-the-art metric for predicting the severity of captions occluding content during videos, which had been based on that prior study. We contribute empirical findings for improving DHH viewers' experience, guiding the placement of captions to minimize occlusions, and automated evaluation of captioning quality in television broadcasts.
AB - Television captions blocking visual information causes dissatisfaction among Deaf and Hard of Hearing (DHH) viewers, yet existing caption evaluation metrics do not consider occlusion. To create such a metric, DHH participants in a recent study imagined how bad it would be if captions blocked various on-screen text or visual content. To gather more ecologically valid data for creating an improved metric, we asked 24 DHH participants to give subjective judgments of caption quality after actually watching videos, and a regression analysis revealed which on-screen contents' occlusion related to users' judgments. For several video genres, a metric based on our new dataset out-performed the prior state-of-the-art metric for predicting the severity of captions occluding content during videos, which had been based on that prior study. We contribute empirical findings for improving DHH viewers' experience, guiding the placement of captions to minimize occlusions, and automated evaluation of captioning quality in television broadcasts.
KW - Accessibility
KW - Caption
KW - Metric
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=85130553652&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130553652&partnerID=8YFLogxK
U2 - 10.1145/3491102.3517681
DO - 10.1145/3491102.3517681
M3 - Conference contribution
AN - SCOPUS:85130553652
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 30 April 2022 through 5 May 2022
ER -