• Title/Summary/Keyword: 벡터기반 크기 패닝

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Interpolation method of head-related transfer function based on the least squares method and an acoustic modeling with a small number of measurement points (최소자승법과 음향학적 모델링 기반의 적은 개수의 측정점에 대한 머리전달함수 보간 기법)

  • Lee, Seokjin
    • The Journal of the Acoustical Society of Korea
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    • v.36 no.5
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    • pp.338-344
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    • 2017
  • In this paper, an interpolation method of HRTF (Head-Related Transfer Function) is proposed for small-sized measurement data set, especially. The proposed algorithm is based on acoustic modeling of HRTFs, and the algorithm tries to interpolate the HRTFs via estimation the model coefficients. However, the estimation of the model coefficients is hard if there is lack of measurement points, so the algorithm solves the problem by a data augmentation using the VBAP (Vector Based Amplitude Panning). Therefore, the proposed algorithm consists of two steps, which are data augmentation step based on VBAP and model coefficients estimation step by least squares method. The proposed algorithm was evaluated by a simulation with a measured data from CIPIC (Center for Image Processing and Integrated Computing) HRTF database, and the simulation results show that the proposed algorithm reduces mean-squared error by 1.5 dB ~ 4 dB than the conventional algorithms.

A Hierarchical Block Matching Algorithm Based on Camera Panning Compensation (카메라 패닝 보상에 기반한 계층적 블록 정합 알고리즘)

  • Gwak, No-Yun;Hwang, Byeong-Won
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.8
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    • pp.2271-2280
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    • 1999
  • In this paper, a variable motion estimation scheme based on HBMA(Hierarchical Block Matching Algorithm) to improve the performance and to reduce heavy computational and transmission load, is presented. The proposed algorithm is composed of four steps. First, block activity for each block is defined using the edge information of differential image between two sequential images, and then average block activity of the present image is found by taking the mean of block activity. Secondly, camera pan compensation is carried out, according to the average activity of the image, in the hierarchical pyramid structure constructed by wavelet transform. Next, the LUT classifying each block into one among Moving, No Moving, Semi-Moving Block according to the block activity compensated camera pan is obtained. Finally, as varying the block size and adaptively selecting the initial search layer and the search range referring to LUT, the proposed variable HBMA can effectively carries out fast motion estimation in the hierarchical pyramid structure. The cost function needed above-mentioned each step is only the block activity defined by the edge information of the differential image in the sequential images.

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