Adaptive Online Learning of Separable Path Graph Transforms for Intra-prediction
arxiv(2024)
摘要
Current video coding standards, including H.264/AVC, HEVC, and VVC, employ
discrete cosine transform (DCT), discrete sine transform (DST), and secondary
to Karhunen-Loeve transforms (KLTs) decorrelate the intra-prediction residuals.
However, the efficiency of these transforms in decorrelation can be limited
when the signal has a non-smooth and non-periodic structure, such as those
occurring in textures with intricate patterns. This paper introduces a novel
adaptive separable path graph-based transform (GBT) that can provide better
decorrelation than the DCT for intra-predicted texture data. The proposed GBT
is learned in an online scenario with sequential K-means clustering, which
groups similar blocks during encoding and decoding to adaptively learn the GBT
for the current block from previously reconstructed areas with similar
characteristics. A signaling overhead is added to the bitstream of each coding
block to indicate the usage of the proposed graph-based transform. We assess
the performance of this method combined with H.264/AVC intra-coding tools and
demonstrate that it can significantly outperform H.264/AVC DCT for
intra-predicted texture data.
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