Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Fast Partition Decision Using Rotation Forest for Intra-Frame Coding in HEVC Screen Content Coding Extension

회전 포레스트 분류기법을 이용한 HEVC 스크린 콘텐츠 화면 내 부호화 조기분할 결정 방법

  • Heo, Jeonghwan (Department of Electronics and Computer Engineering, Hanyang University) ;
  • Jeong, Jechang (Department of Electronics and Computer Engineering, Hanyang University)
  • 허정환 (한양대학교 전기전자공학부) ;
  • 정제창 (한양대학교 전기전자공학부)
  • Received : 2017.11.10
  • Accepted : 2017.12.05
  • Published : 2018.01.30
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Abstract

This paper presents a fast partition decision framework for High Efficiency Video Coding (HEVC) Screen Content Coding (SCC) based on machine learning. Currently, the HEVC performs quad-tree block partitioning process to achieve optimal coding efficiency. Since this process requires a high computational complexity of the encoding device, the fast encoding process has been studied as determining the block structure early. However, in the case of the screen content video coding, it is difficult to apply the conventional early partition decision method because it shows different partition characteristics from natural content. The proposed method solves the problem by classifying the screen content blocks after partition decision, and it shows an increase of 3.11% BD-BR and 42% time reduction compared to the SCC common test condition.

본 논문에서는 머신러닝을 기반으로 한 조기분할 결정 방법을 통하여 High Efficiency Video Coding (HEVC) Screen Content Coding (SCC) 부호화 기기의 속도를 향상시키는 방법을 제안한다. 현재 HEVC에서는 최적의 부호화 효율을 내기 위해 쿼드트리 블록 분할 과정을 수행한다. 이 과정은 부호화기의 높은 계산 복잡도를 요구하기 때문에 블록 구조를 조기에 결정하여 부호화 속도를 향상시키는 방법으로 고속화 연구가 이루어져 왔다. 하지만 스크린 콘텐츠 부호화는 기존의 자연영상에 맞춰진 부호화 과정과 다른 블록 분할 특성을 보이기 때문에 기존의 조기분할 결정 연구를 적용하기 어렵다. 제안하는 방법은 먼저 스크린 콘텐츠 블록을 분류해 낸 다음 다시 블록분할을 결정하는 방법으로 문제를 해결하였고 SCC 공통 실험 조건에서 3.11%의 BD-BR 증가와 평균 42%의 부호화 시간 감소를 보였다.

Keywords

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