Compression standards have been used to reduce the cost of image storage and
transmission for decades. In recent years, learned image compression methods
have been proposed and achieved compelling performance to the traditional
standards. However, in these methods, a set of different networks are used for
various compression rates, resulting in a high cost in model storage and
training. Although some variable-rate approaches have been proposed to reduce
the cost by using a single network, most of them brought some performance
degradation when applying fine rate control. To enable variable-rate control
without sacrificing the performance, we propose an efficient Interpolation
Variable-Rate (IVR) network, by introducing a handy Interpolation Channel
Attention (InterpCA) module in the compression network. With the use of two
hyperparameters for rate control and linear interpolation, the InterpCA
achieves a fine PSNR interval of 0.001 dB and a fine rate interval of 0.0001
Bits-Per-Pixel (BPP) with 9000 rates in the IVR network. Experimental results
demonstrate that the IVR network is the first variable-rate learned method that
outperforms VTM 9.0 (intra) in PSNR and Multiscale Structural Similarity
(MS-SSIM).

本研究提出了一种高效的插值可变率（IVR）网络，通过引入 Interpolation Channel Attention（InterpCA）模块来实现可变率控制，实验结果表明 IVR 网络是第一个在 PSNR 和 Multiscale Structural Similarity（MS-SSIM）方面优于 VTM9.0 的可变率学习方法。