Fast H.264/AVC to HEVC transcoding based on residual homogeneity

Zheng, Feiyang; Shi, Zhanbiao; Zhang, Xiaoyun; Gao, Zhiyong

doi:10.1109/icalip.2014.7009898

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Jiang et al [4] classified one frame into three regions according to coding complexity, in order to decide the CU depth and PU mode. In [5], the residual homogeneity indicator is used for CU partition in a transcoding. Peixoto used the similarity of the motion vectors (MVs) in H.264/AVC to decide which HEVC CU partitions should be tested [6].…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Qing

et al. 2019

IET Image Processing

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Section: Introductionmentioning

confidence: 99%

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

Lin

Qing

et al. 2019

IET Image Processing

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Fast HEVC to SCC Transcoder by Early CU Partitioning Termination and Decision Tree-Based Flexible Mode Decision for Intra-Frame Coding

2019

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Screen content coding (SCC) was developed to enhance High Efficiency Video Coding (HEVC) for encoding screen content videos. However, HEVC has dominated the market for many years, and it leaves many legacy screen content videos encoded by HEVC. Therefore, it is desired that the legacy screen content videos are migrated from HEVC to SCC to improve the coding efficiency. This paper presents a fast transcoding algorithm by analyzing various features from four categories. They are the features from the HEVC decoder, static features, dynamic features, and spatial features. First, the coding unit (CU) depth level collected from the HEVC decoder is utilized to early terminate the CU partition in SCC. Second, a flexible encoding structure is proposed to make early mode decisions with the help of various features. On the one hand, high decision accuracy is achieved because mode decision is considered from different aspects by utilizing features from more than one category. On the other hand, high computational complexity is reduced because the flexible structure considers the decision of each mode separately. The experimental results show that the proposed algorithm provides 51.24% and 54.65% re-encoding time reduction with 1.32% and 1.25% negligible Bjøntegaard delta bitrate loss for YUV 4:2:0 and YUV 4:4:4 screen content sequences using all-intra configuration, respectively. INDEX TERMS Transcoding, screen content coding (SCC), High Efficiency Video Coding (HEVC), fast algorithm, machine learning.

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