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Enhanced Prediction for Low Complexity Near-lossless Compression

낮은 복잡도의 준무손실 압축을 위한 향상된 예측 기법

  • Received : 2014.01.22
  • Accepted : 2014.03.04
  • Published : 2014.03.30

Abstract

This paper proposes an enhance prediction for conventional near-lossless coder to effectively lower external memory bandwidth in image processing SoC. First, we utilize an already reconstructed green component as a base of predictor of the other color component because high correlation between RGB color components usually exists. Next, we can improve prediction performance by applying variable block size prediction. Lastly, we use minimum internal memory and improve a temporal prediction performance by using a template dictionary that is sampled in previous frame. Experimental results show that the proposed algorithm shows better performance than the previous works. Natural images have approximately 30% improvement in coding efficiency and CG images have 60% improvement on average.

본 논문은 영상처리용 SoC에서 외부 메모리 대역폭을 효과적으로 낮추기 위한 near-lossless 이미지 코더의 압축 성능을 향상시키는 새로운 예측 기법을 제안한다. 먼저, RGB 간 correlation을 고려하여 이미 복원된 G 성분을 기반으로 R과 B 성분을 효과적으로 예측하는 inter-color prediction을 수행한다. 다음으로 가변 블록 예측을 통해 예측 성능을 향상시킨다. 마지막으로 이전 프레임에서 sampling된 템플릿 dictionary를 이용해 G 성분 예측 시 최소한의 내부 메모리만을 사용하여 시간 축 예측 성능을 개선시키는 방법을 제안한다. 실험 결과를 통해 자연 영상의 경우 기존 기법 대비 평균적으로 약 30%의 코딩 효율 향상을 보이고, CG 영상의 경우에는 평균 60% 정도의 성능 향상을 보임을 알 수 있다.

Keywords

References

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