Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Weight Step Video Compression   A variety of compression methods based on encoding images as weights of a
neural network have been recently proposed. Yet, the potential of similar
approaches for video compression remains unexplored. In this work, we suggest a
set of experiments for testing the feasibility of compressing video using two
architectural paradigms, coordinate-based MLP (CbMLP) and convolutional
network. Furthermore, we propose a novel technique of neural weight stepping,
where subsequent frames of a video are encoded as low-entropy parameter
updates. To assess the feasibility of the considered approaches, we will test
the video compression performance on several high-resolution video datasets and
compare against existing conventional and neural compression techniques.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex