JOURNAL ARTICLE

Motion Vector Predictor selection method for multi-view video coding

Abstract

본 논문에서는 JVT에서 표준화가 진행 중인 다시점 비디오 부호화의 부호화 효율을 높이기 위해 다시점 비디오의 예측구조의 특성을 고려하여 움직임벡터 예측값을 선택하는 방법을 제안한다. 다시점 비디오 부호화에서 시간 방향과 뷰 방향의 예측을 수행하면서 서로 다른 성향의 움직임벡터가 발생된다. 또한 주변 블록에서 시간 방향과 뷰 방향 참조의 움직임벡터가 혼재하는 상황에서 동일한 성향의 움직임벡터가 선택되지 않는 경우가 발생되어 움직임벡터의 예측 효율이 저하된다. 이에 다시점 비디오 부호화의 예측구조에 따라 움직임벡터 예측값을 선택할 수 있는 방법을 제안한다. 제안하는 움직임벡터 예측값 선택 방법을 통하여 다시점 비디오 부호화의 압축 효율을 높일 수 있었으며, JMVM 3.6에서 움직임벡터 예측값을 선택하는 방법을 사용하는 다시점 비디오 부호화와 비교하여 PSNR(Peak Signal-to-Noise Ratio)이 약$0.03{\sim}0.1$ dB 정도 향상되었다. In this paper, we propose a method to select motion vector predictor by considering prediction structure of a multi view content for coding efficiency of multi view coding which is being standardized in JVT. Motion vector of a different tendency is happened while carrying out temporal and view reference prediction of multi-view video coding. Also, due to the phenomena of motion vectors being searched in both temporal and view order, the motion vectors do not agree with each other resulting a decline in coding efficiency. This paper is about how the motion vector predictor are selected with information of prediction structure. By using the proposed method, a compression ratio of the proposed method in multi-view video coding is increased, and finally $0.03{\sim}0.1$ dB PSNR(Peak Signal-to-Noise Ratio) improvement was obtained compared with the case of JMVM 3.6 method.

Keywords:
Motion vector Computer science Coding (social sciences) Quarter-pixel motion Motion compensation Artificial intelligence Motion estimation Computer vision Algorithmic efficiency Block-matching algorithm Peak signal-to-noise ratio Data compression Pattern recognition (psychology) Mathematics Video tracking Video processing Statistics Image (mathematics)

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Topics

Video Coding and Compression Technologies
Physical Sciences →  Computer Science →  Signal Processing
Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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