한국산업정보학회논문지 (Journal of Korea Society of Industrial Information Systems)

한국산업정보학회 (Korea Society of Industrial Information Systems)
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깊이 얼굴 영상 부호화에서의 양자화 인자 결정 방법

Quantization Parameter Determination Method for Face Depth Image Encoding

  • 박동진 (동의대학교 컴퓨터소프트웨어공학과) ;
  • 권순각 (동의대학교 컴퓨터소프트웨어공학과)
  • 투고 : 2020.01.10
  • 심사 : 2020.02.02
  • 발행 : 2020.02.29
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초록

본 논문에서는 얼굴 인식 정확도에 미치는 영향을 최소화하면서 효율적으로 깊이 얼굴 영상을 압축하기 위한 양자화 변수 결정 방법을 제안한다. H.264/AVC의 양자화를 적용하여 깊이 얼굴 영상을 압축 할 때 얼굴 특징을 최대한 유지할 수 있도록 타원체 모델링의 예측 정확도와 각각의 양자화 단위 블록의 얼굴 인식에서의 중요도를 이용하여 양자화 인자를 차등적으로 부여한다. 모의실험 결과 제안된 방법을 통해 같은 압축율에서 얼굴 인식 성공률이 최대 6% 개선되었다.

In this paper, we propose a quantization parameter determination method for face depth image encoding in order to minimize an impact on a face recognition accuracy. When a face depth image is compressed through quantization in H.264/AVC, differential quantization parameters are assigned according to an accuracy of ellipsoid modeling prediction and an importance degree of a unit block in extracting facial features. The simulation results show that the face recognition success rates are improved by up to 6% at the same compression rates through the proposed compression rate determination method.

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참고문헌

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