For members of the Deaf Community in the United States, current communication tools include TTY/TTD services, video relay services, and text-based communication. With the growth of cellular technology, mobile sign language conversations are becoming a possibility. Proper coding techniques must be employed to compress American Sign Language (ASL) video for low-rate transmission while maintaining the quality of the conversation. In order to evaluate these techniques, an appropriate quality metric is needed. This paper demonstrates that traditional video quality metrics, such as PSNR, fail to predict subjective intelligibility scores. By considering the unique structure of ASL video, an appropriate objective metric is developed. Face and hand segmentation is performed using skin-color detection techniques. The distortions in the face and hand regions are optimally weighted and pooled across all frames to create an objective intelligibility score for a distorted sequence. The objective intelligibility metric performs significantly better than PSNR in terms of correlation with subjective responses.
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