V-DMC DISPLACEMENT VECTOR INTEGER QUANTIZATION

    公开(公告)号:US20240357144A1

    公开(公告)日:2024-10-24

    申请号:US18638221

    申请日:2024-04-17

    摘要: A device for decoding encoded mesh data determines, based on the encoded mesh data, a base mesh; determine, based on the encoded mesh data, a set of coefficients; receives in the encoded mesh data a quantization parameter value; determines an inverse scaling factor based on the quantization parameter value; performs an inverse scaling, based on the inverse scaling factor and using integer precision arithmetic, on the set of coefficients to determine a set of de-quantized coefficients; determines a displacement vector based on the set of de-quantized coefficients; deforms the base mesh based on the displacement vector to determine a decoded mesh; and outputs the decoded mesh.

    METHOD FOR EMBEDDING THE HAPTICS EFFECTS SEMANTIC IN HAPTICS STREAMING

    公开(公告)号:US20240348812A1

    公开(公告)日:2024-10-17

    申请号:US18636607

    申请日:2024-04-16

    发明人: Iraj SODAGAR

    摘要: Method, apparatus, and system for haptic signal processing are provided. The process may include receiving a media stream comprising at least one haptic track and at least one video track. The process may include receiving a media stream, in a case that the data is in the haptics interchange format, obtaining a first syntax, from the media stream, and determining, from the first syntax, a scheme used for a semantic description of effects of the media stream, in a case that the data is in the haptics streaming format, obtaining a second syntax, from the media stream, and determining, from the second syntax, the scheme used for a semantic description of effects of the media stream and a number of characters in the scheme, and controlling decoding of the media stream based determining the scheme.

    SIMPLIFICATIONS OF CROSS-COMPONENT LINEAR MODEL

    公开(公告)号:US20240348802A1

    公开(公告)日:2024-10-17

    申请号:US18750699

    申请日:2024-06-21

    摘要: A computing device performs a method of decoding video data by reconstructing a luma block corresponding to a chroma block; searching a sub-group of a plurality of reconstructed neighboring luma samples in a predefined order to identify a maximum luma sample and a minimum luma sample; computing a down-sampled maximum luma sample corresponding to the maximum luma sample; computing a down-sampled minimum luma sample corresponding to the minimum luma sample; generating a linear model using the down-sampled maximum luma sample, the down-sampled minimum luma sample, the first reconstructed chroma sample, and the second reconstructed chroma sample; computing down-sampled luma samples from luma samples of the reconstructed luma block, wherein each down-sampled luma sample corresponds to a chroma sample of the chroma block; and predicting chroma samples of the chroma block by applying the liner model to the corresponding down-sampled luma samples.