Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lossless Compression of Mosaic Images with Convolutional Neural Network
  Prediction   We present a CNN-based predictive lossless compression scheme for raw color
mosaic images of digital cameras. This specialized application problem was
previously understudied but it is now becoming increasingly important, because
modern CNN methods for image restoration tasks (e.g., superresolution, low
lighting enhancement, deblurring), must operate on original raw mosaic images
to obtain the best possible results. The key innovation of this paper is a
high-order nonlinear CNN predictor of spatial-spectral mosaic patterns. The
deep learning prediction can model highly complex sample dependencies in
spatial-spectral mosaic images more accurately and hence remove statistical
redundancies more thoroughly than existing image predictors. Experiments show
that the proposed CNN predictor achieves unprecedented lossless compression
performance on camera raw images.

29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th