Rate Distortion Characteristic Modeling for Neural Image Compression
- URL: http://arxiv.org/abs/2106.12954v1
- Date: Thu, 24 Jun 2021 12:23:05 GMT
- Title: Rate Distortion Characteristic Modeling for Neural Image Compression
- Authors: Chuanmin Jia, Ziqing Ge, Shanshe Wang, Siwei Ma, Wen Gao
- Abstract summary: End-to-end optimization capability offers neural image compression (NIC) superior lossy compression performance.
distinct models are required to be trained to reach different points in the rate-distortion (R-D) space.
We make efforts to formulate the essential mathematical functions to describe the R-D behavior of NIC using deep network and statistical modeling.
- Score: 59.25700168404325
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: End-to-end optimization capability offers neural image compression (NIC)
superior lossy compression performance. However, distinct models are required
to be trained to reach different points in the rate-distortion (R-D) space. In
this paper, we consider the problem of R-D characteristic analysis and modeling
for NIC. We make efforts to formulate the essential mathematical functions to
describe the R-D behavior of NIC using deep network and statistical modeling.
Thus continuous bit-rate points could be elegantly realized by leveraging such
model via a single trained network. In this regard, we propose a plugin-in
module to learn the relationship between the target bit-rate and the binary
representation for the latent variable of auto-encoder. Furthermore, we model
the rate and distortion characteristic of NIC as a function of the coding
parameter $\lambda$ respectively. Our experiments show our proposed method is
easy to adopt and obtains competitive coding performance with fixed-rate coding
approaches, which would benefit the practical deployment of NIC. In addition,
the proposed model could be applied to NIC rate control with limited bit-rate
error using a single network.
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