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Coding unit complexity-based predictions of coding unit depth and prediction unit mode for efficient HEVC-to-SHVC transcoding with quality scalability.

Authors :
Yeh, Chia-Hung
Tseng, Wen-Yu
Kang, Li-Wei
Lee, Cheng-Wei
Muchtar, Kahlil
Chen, Mei-Juan
Source :
Journal of Visual Communication & Image Representation. Aug2018, Vol. 55, p342-351. 10p.
Publication Year :
2018

Abstract

Highlights • Novel unit prediction method for efficient HEVC-to-SHVC transcoding is proposed. • Two proposed techniques are early termination and adaptive confidence interval. • We are among the first to propose a fast HEVC-to-SHVC transcoding framework. • Proposed method reduces encoding time of SHVC by 74.14% almost without quality loss. 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10473203
Volume :
55
Database :
Academic Search Index
Journal :
Journal of Visual Communication & Image Representation
Publication Type :
Academic Journal
Accession number :
131628604
Full Text :
https://doi.org/10.1016/j.jvcir.2018.06.008