Learned Image Compression (LIC) has shown remarkable progress in recent
years. Existing works commonly employ CNN-based or self-attention-based modules
as transform methods for compression. However, there is no prior research on
neural transform that focuses on specific regions. In response, we introduce
the class-agnostic segmentation masks (i.e. semantic masks without category
labels) for extracting region-adaptive contextual information. Our proposed
module, Region-Adaptive Transform, applies adaptive convolutions on different
regions guided by the masks. Additionally, we introduce a plug-and-play module
named Scale Affine Layer to incorporate rich contexts from various regions.
While there have been prior image compression efforts that involve segmentation
masks as additional intermediate inputs, our approach differs significantly
from them. Our advantages lie in that, to avoid extra bitrate overhead, we
treat these masks as privilege information, which is accessible during the
model training stage but not required during the inference phase. To the best
of our knowledge, we are the first to employ class-agnostic masks as privilege
information and achieve superior performance in pixel-fidelity metrics, such as
Peak Signal to Noise Ratio (PSNR). The experimental results demonstrate our
improvement compared to previously well-performing methods, with about 8.2%
bitrate saving compared to VTM-17.0. The code will be released at
this https URL

通过使用类别 - 不可知分割掩模和区域适应转换，本文提出了一种改进的学习图像压缩方法，以提高像素保真度和比特率节省。