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Author:

Mou, Luntian (Mou, Luntian.) | Li, Peize (Li, Peize.) | Zhao, Haiwu (Zhao, Haiwu.) | Fu, Qiang (Fu, Qiang.) | Luo, Hong (Luo, Hong.) | Liu, Cong (Liu, Cong.) | Ma, Nan (Ma, Nan.) | Huang, Tiejun (Huang, Tiejun.) | Gao, Wen (Gao, Wen.)

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EI Scopus

Abstract:

As the text-based visual representation of a program’s audio elements, Closed Captioning primarily serves as a technology to enhance communication for the hearing impaired. Since text is much simpler than audio and video, Closed Captioning is traditionally transmitted as supplementary or auxiliary information as part of the image or in an extended or private data field of an encoded video bitstream called video elementary stream, usually accompanied by one or more audio elementary streams. Since Closed Captioning is extremely important for the accessibility of the audio content of a program to the hearing impaired, we propose to encode the closed caption into a bitstream called caption elementary stream, which can fly together with audio and video elementary streams. In other words, closed caption can be stored and transmitted in a manner similar to how audio and video are handled. We have drafted a national standard for Closed Captioning in China, which is now in its final stage of approval and publication. In this paper, the main technical content of the emerging Closed Captioning standard will be introduced. Specifically, the encoding, storage, and transmission of Closed Captioning will be described. Moreover, the decoding and presentation of Closed Captioning under the two scenarios of on demand streaming and live streaming will also be designed and discussed. The AI technology of Speech-to-Text enables Closed Captioning to be implemented efficiently with the help of manual proofreading. Positively, the emergence of the Closed Captioning standard will enhance accessibility to audio-visual programs on both the broadcasting network and the Internet for the hearing-impaired in China and worldwide. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keyword:

Image coding Image enhancement Video on demand Video streaming Encoding (symbols) Audio acoustics Audio streaming Signal encoding Speech enhancement Energy security

Author Community:

  • [ 1 ] [Mou, Luntian]Beijing University of Technology, Beijing, China
  • [ 2 ] [Mou, Luntian]Beijing Institute of Artificial Intelligence, Beijing, China
  • [ 3 ] [Li, Peize]Beijing University of Technology, Beijing, China
  • [ 4 ] [Zhao, Haiwu]Shanghai University of Engineering Science, Shanghai, China
  • [ 5 ] [Fu, Qiang]Photosynthetic AI Tech Co., Ltd., Hangzhou, China
  • [ 6 ] [Luo, Hong]China Mobile Information Technology Co., Ltd., Hangzhou, China
  • [ 7 ] [Liu, Cong]IFLYTEK Research, Hefei, China
  • [ 8 ] [Ma, Nan]Beijing University of Technology, Beijing, China
  • [ 9 ] [Ma, Nan]Beijing Institute of Artificial Intelligence, Beijing, China
  • [ 10 ] [Huang, Tiejun]Peking University, Beijing, China
  • [ 11 ] [Gao, Wen]Peking University, Beijing, China
  • [ 12 ] [Gao, Wen]Peng Cheng Laboratory, Shenzhen, China

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Source :

ISSN: 0302-9743

Year: 2025

Volume: 15170 LNAI

Page: 282-292

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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