Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis-Reference-Cited by-同舟云学术

Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis

Published:2019-01 Issue:1 Volume:13 Page:34-43
ISSN:1751-9667
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Lin Hongwei¹²,He Xiaohai¹,Qing Linbo¹,Su Shan¹,Xiong Shuhua¹

Affiliation:

1. College of Electronics and Information Engineering, Sichuan UniversityNo. 24 South Section 1ChengduPeople's Republic of China

2. College of Electrical Engineering, Northwest University for NationalitiesLanzhouPeople's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2018.5703

Reference27 articles.

1. Overview of the H.264/AVC video coding standard

2. Comparison of the Coding Efficiency of Video Coding Standards—Including High Efficiency Video Coding (HEVC)

3. Zero‐block inter/intra mode decision for MPEG‐2 to H.264/AVC inter P‐frame transcoding;Su C.J.;IET Image Process.,2010

4. Fast transcoding from H.264 to HEVC based on region feature analysis;Jiang W.;Multimedia Tools Appl.,2014

5. 2014 Proc. Int. Conf. Audio Language and Image Processing Shanghai China F. Zheng Z. Shi X. Zhang Fast H.264/AVC to HEVC transcoding based on residual homogeneity 765 770

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