Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applications. To overcome this limitation, some previous works propose to semantically structure the bitstream, which can meet specific application requirements by selective transmission and reconstruction. Nevertheless, they divide the input image into multiple rectangular regions according to semantics and ignore avoiding information interaction among them, causing waste of bitrate and distorted reconstruction of region boundaries. In this paper, we propose to decouple an image into multiple groups with irregular shapes based on a customized group mask and compress them independently. Our group mask describes the image at a finer granularity, enabling significant bitrate saving by reducing the transmission of redundant content. Moreover, to ensure the fidelity of selective reconstruction, this paper proposes the concept of group-independent transform that maintain the independence among distinct groups. And we instantiate it by the proposed Group-Independent Swin-Block (GI Swin-Block). Experimental results demonstrate that our framework structures the bitstream with negligible cost, and exhibits superior performance on both visual quality and intelligent task supporting.

翻译：图像压缩技术通常侧重于压缩矩形图像以供人类消费，然而这会导致下游应用中传输冗余内容。为解决此限制，部分先前工作提出对比特流进行语义结构化处理，通过选择性传输与重建满足特定应用需求。然而，这些方法根据语义将输入图像划分为多个矩形区域，但未能避免区域间的信息交互，导致比特率浪费和区域边界重建失真。本文提出基于定制分组掩码将图像解耦为多个不规则形状的组，并对其进行独立压缩。所设计的分组掩码以更细粒度描述图像，通过减少冗余内容的传输实现显著的比特率节省。此外，为确保选择性重建的保真度，本文提出组独立变换的概念以保持不同组间的独立性，并通过所提出的组独立Swin块（GI Swin-Block）对其进行实例化。实验结果表明，本框架以可忽略的代价对比特流进行结构化处理，在视觉质量与智能任务支持方面均展现出优越性能。