EFFICIENT MULTI-VIEW CODING USING DEPTH-MAP ESTIMATE AND UPDATE

    公开(公告)号:US20180376127A1

    公开(公告)日:2018-12-27

    申请号:US16120731

    申请日:2018-09-04

    Abstract: The missing of a depth map for a current picture of a reference view—due to the transmission thereof being not anticipated anyway, or due to the advantageous coding order between a texture/picture and its depth map, or due an anticipated discarding of depth data from the bitstream during transmission or decoding—may be adequately addressed so as to reduce inter-view redundancies by estimating a depth map for the pictures of the reference and dependent views and updating same using motion and/or disparity data signaled within the multi-view data stream. In particular, virtually all multi-view data streams have random access points defined therein, i.e. time instances corresponding to pictures of the views of the multi-view signal which are coded without temporal prediction and other dependencies to previously coded pictures, but merely using intra prediction as far as the reference view is concerned, and intra prediction as well as disparity-based prediction as far as the dependent view is concerned. Accordingly, the disparity data signaled within the multi-view data stream for inter-view prediction is exploited to initialize a depth map estimate for the dependent view, and this primary depth map estimate is consecutively updated during the further course of the multi-view coding using motion data and/or disparity data signal within the multi-view data stream. The thus obtained depth map estimate continuously updated, enables the dependent various methods of inter-view redundancy reduction to be performed in a more efficient way than without having access to this depth map estimate. According to another aspect, the following discovery is exploited: the overhead associated with an enlarged list of motion predictor candidates for a block of a picture of a dependent view is comparatively low compared to a gain in motion vector prediction quality resulting from an adding of a motion vector candidate which is determined from an, in disparity-compensated sense, co-located block of a reference view.

    CONTEXT INITIALIZATION IN ENTROPY CODING
    169.
    发明申请
    CONTEXT INITIALIZATION IN ENTROPY CODING 有权
    入侵编码中的上下文初始化

    公开(公告)号:US20160366447A1

    公开(公告)日:2016-12-15

    申请号:US15244126

    申请日:2016-08-23

    Abstract: A decoder includes an entropy decoder configured to derive a number of bins of the binarizations from the data stream using binary entropy decoding by selecting a context among different contexts and updating probability states associated with the different contexts, dependent on previously decoded portions of the data stream; a desymbolizer configured to debinarize the binarizations of the syntax elements to obtain integer values of the syntax elements; a reconstructor configured to reconstruct the video based on the integer values of the syntax elements using a quantization parameter, wherein the entropy decoder is configured to distinguish between 126 probability states and to initialize the probability states associated with the different contexts according to a linear equation of the quantization parameter, wherein the entropy decoder is configured to, for each of the different contexts, derive a slope and an offset of the linear equation from first and second four bit parts of a respective 8 bit initialization value.

    Abstract translation: 解码器包括熵解码器,其被配置为根据数据流的先前解码部分,根据数据流的先前解码部分,通过使用二进制熵解码从不同上下文中选择上下文并更新与不同上下文相关联的概率状态,从数据流中导出二进制数的二进制数 ; 配音器,被配置为对语法元素的二进制化进行修饰以获得语法元素的整数值; 重建器,被配置为使用量化参数基于所述语法元素的整数值来重构所述视频,其中所述熵解码器被配置为区分126概率状态,并且根据线性方程式来初始化与所述不同上下文相关联的概率状态 所述量化参数,其中所述熵解码器被配置为针对所述不同上下文中的每一者,导出所述线性方程与相应8位初始化值的第一和第四四位部分的斜率和偏移。

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