Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Generation of Blocks for Video Coding   Well-trained generative neural networks (GNN) are very efficient at
compressing visual information for static images in their learned parameters
but not as efficient as inter- and intra-prediction for most video content.
However, for content entering a frame, such as during panning or zooming out,
and content with curves, irregular shapes, or fine detail, generation by a GNN
can give better compression efficiency (lower rate-distortion). This paper
proposes encoding content-specific learned parameters of a GNN within a video
bitstream at specific times and using the GNN to generate content for specific
ranges of blocks and frames. The blocks to generate are just the ones for which
generation gives more efficient compression than inter- or intra- prediction.
This approach maximizes the usefulness of the information contained in the
learned parameters.

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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys