In recent years, neural network-driven image compression (NIC) has gained
significant attention. Some works adopt deep generative models such as GANs and
diffusion models to enhance perceptual quality (realism). A critical obstacle
of these generative NIC methods is that each model is optimized for a single
bit rate. Consequently, multiple models are required to compress images to
different bit rates, which is impractical for real-world applications. To
tackle this issue, we propose a variable-rate generative NIC model.
Specifically, we explore several discriminator designs tailored for the
variable-rate approach and introduce a novel adversarial loss. Moreover, by
incorporating the newly proposed multi-realism technique, our method allows the
users to adjust the bit rate, distortion, and realism with a single model,
achieving ultra-controllability. Unlike existing variable-rate generative NIC
models, our method matches or surpasses the performance of state-of-the-art
single-rate generative NIC models while covering a wide range of bit rates
using just one model. Code will be available at this https URL

近年来，神经网络驱动的图像压缩（NIC）方法备受关注。本文提出了一种可变比率的生成式 NIC 模型，通过多种鉴别器设计和引入新的对抗损失，结合新提出的多真实感技术，用户能够通过单一模型灵活地调整比特率、失真和真实感，实现了极高的可控性并覆盖宽范围的比特率。与已有可变比率生成式 NIC 模型不同的是，本方法在只使用一个模型的情况下，达到或超过最先进的单比率生成式 NIC 模型的性能。

控制速率、失真和逼真度：走向一个全面的神经图像压缩模型

Controlling Rate, Distortion, and Realism: Towards a Single  Comprehensive Neural Image Compression Model

Massive multiple-input multiple-output (MIMO) is a promising technology to
increase link capacity and energy efficiency. However, these benefits are based
on available channel state information (CSI) at the base station (BS).
Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS,
thereby consuming precious bandwidth resource. Large-scale antennas at the BS
for massive MIMO seriously increase this overhead. In this paper, we propose a
multiple-rate compressive sensing neural network framework to compress and
quantize the CSI. This framework not only improves reconstruction accuracy but
also decreases storage space at the UE, thus enhancing the system feasibility.
Specifically, we establish two network design principles for CSI feedback,
propose a new network architecture, CsiNet+, according to these principles, and
develop a novel quantization framework and training strategy. Next, we further
introduce two different variable-rate approaches, namely, SM-CsiNet+ and
PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%,
respectively. Experimental results show that CsiNet+ outperforms the
state-of-the-art network by a margin but only slightly increases the parameter
number. We also investigate the compression and reconstruction mechanism behind
deep learning-based CSI feedback methods via parameter visualization, which
provides a guideline for subsequent research.

本文提出了一种多速度压缩感知神经网络框架来压缩和量化 CSI，从而提高重建精度、减少存储空间，增强系统可行性，并通过可视化参数研究了基于深度学习的 CSI 反馈方法的压缩和重建机制，提供了随后的研究指导。