Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Substitutional Neural Image Compression   We describe Substitutional Neural Image Compression (SNIC), a general
approach for enhancing any neural image compression model, that requires no
data or additional tuning of the trained model. It boosts compression
performance toward a flexible distortion metric and enables bit-rate control
using a single model instance. The key idea is to replace the image to be
compressed with a substitutional one that outperforms the original one in a
desired way. Finding such a substitute is inherently difficult for conventional
codecs, yet surprisingly favorable for neural compression models thanks to
their fully differentiable structures. With gradients of a particular loss
backpropogated to the input, a desired substitute can be efficiently crafted
iteratively. We demonstrate the effectiveness of SNIC, when combined with
various neural compression models and target metrics, in improving compression
quality and performing bit-rate control measured by rate-distortion curves.
Empirical results of control precision and generation speed are also discussed.

23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex