@article{c3e180d2931a4ad49909316588f3bf5d,
title = "Coding unit complexity-based predictions of coding unit depth and prediction unit mode for efficient HEVC-to-SHVC transcoding with quality scalability",
abstract = "To support good video quality of experiences in heterogeneous environments, transcoding an existed HEVC (high efficiency video coding) video bitstream to a SHVC (scalability extension of HEVC) bitstream with quality scalability is highly required. A straightforward way is to first fully decode the input HEVC bitstream and then fully re-encode it with the SHVC encoder, which requires a tremendous computational complexity. To solve the problem, in this paper, a coding unit complexity (CUC)-based prediction method for predictions of CU (coding unit) depth and PU (prediction unit) mode for efficient HEVC-to-SHVC transcoding with quality scalability is proposed to significantly reduce the transcoding complexity. The proposed method contains two prediction techniques, including (i) early termination and (ii) adaptive confidence interval, and predicts the CU depth and PU mode relying on the decoded information from the input HEVC bitstream. Experimental results have shown that the proposed method significantly outperforms the traditional HEVC-to-SHVC method by 74.14% on average in reductions of encoding time for SHVC enhancement layer.",
keywords = "Coding unit complexity, Early termination, HEVC (high efficiency video coding), SHVC (scalability extension of HEVC), Scalable video coding, Video transcoding",
author = "Yeh, {Chia Hung} and Tseng, {Wen Yu} and Kang, {Li Wei} and Lee, {Cheng Wei} and Kahlil Muchtar and Chen, {Mei Juan}",
note = "Funding Information: This work was supported in part by Ministry of Science and Technology , Taiwan, under the Grants NSC 102-2221-E-110-032-MY3 , MOST 103-2221-E-110-045-MY3 , MOST 103-2221-E-003-034-MY3 , MOST 105-2221-E-003-030-MY3 , and MOST 105-2628-E-224-001-MY3 . This work was also financially supported by the “Artificial Intelligence Recognition Industry Service Research Center (Project No.107-N04-2)” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education ( MOE ) in Taiwan. Funding Information: This work was supported in part by Ministry of Science and Technology, Taiwan, under the Grants NSC 102-2221-E-110-032-MY3, MOST 103-2221-E-110-045-MY3, MOST 103-2221-E-003-034-MY3, MOST 105-2221-E-003-030-MY3, and MOST 105-2628-E-224-001-MY3. This work was also financially supported by the “Artificial Intelligence Recognition Industry Service Research Center (Project No.107-N04-2)” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Publisher Copyright: {\textcopyright} 2018 Elsevier Inc.",
year = "2018",
month = aug,
doi = "10.1016/j.jvcir.2018.06.008",
language = "English",
volume = "55",
pages = "342--351",
journal = "Journal of Visual Communication and Image Representation",
issn = "1047-3203",
publisher = "Academic Press Inc.",
}