Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatial Information Refinement for Chroma Intra Prediction in Video
  Coding   Video compression benefits from advanced chroma intra prediction methods,
such as the Cross-Component Linear Model (CCLM) which uses linear models to
approximate the relationship between the luma and chroma components. Recently
it has been proven that advanced cross-component prediction methods based on
Neural Networks (NN) can bring additional coding gains. In this paper, spatial
information refinement is proposed for improving NN-based chroma intra
prediction. Specifically, the performance of chroma intra prediction can be
improved by refined down-sampling or by incorporating location information.
Experimental results show that the two proposed methods obtain 0.31%, 2.64%,
2.02% and 0.33%, 3.00%, 2.12% BD-rate reduction on Y, Cb and Cr components,
respectively, under All-Intra configuration, when implemented in Versatile
Video Coding (H.266/VVC) test model. Index Terms-Chroma intra prediction,
convolutional neural networks, spatial information refinement.

14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph