逐步学习图像压缩用于机器感知
Progressive Learned Image Compression for Machine Perception
AI总结：
本文提出PICM-Net，一种基于三平面编码的逐步学习图像压缩算法，通过自适应解码控制器提升机器感知任务的适应性和性能。
摘要：
最近在学习图像编码器方面的发展已经从人类感知扩展到机器感知。然而，具有细粒度可扩展性（FGS）的逐步图像压缩——这允许解码单个位流在多个质量级别上解码——仍未被探索用于机器导向的编码器。在本工作中，我们提出了一种新的逐步学习图像压缩编码器用于机器感知，PICM-Net，基于三平面编码。通过分析人类和机器导向的速率-失真优先级之间的差异，我们系统地研究了机器导向编码器中的潜在优先策略。为了进一步提高现实适应性，我们设计了一个自适应解码控制器，该控制器在推理时间动态确定解码级别，以保持下游机器预测所需的置信度。大量实验表明，我们的方法在保持下游分类任务高性能的同时，实现了高效且适应性的逐步传输，为机器感知的逐步图像压缩建立了新范式。
英文摘要：
Recent advances in learned image codecs have extended from human perception toward machine perception However, progressive image compression with fine granular scalability (FGS)-which enables decoding a single bitstream at multiple quality levels-remains unexplored for machine-oriented codecs. In this work, we propose PICM-Net, a progressive learned image compression codec for machine perception built on trit-plane coding. Starting from a human-oriented codec, we adapt it for machine perception via a spatial-frequency modulation adapter (SFMA), a hyper-synthesis low-rank adapter (HSLoRA), and progressive decoding-aware training, and analyze how task-driven adaptation alters symbol prioritization for progressive transmission. To further support real-world deployment, we introduce an adaptive decoding controller that dynamically determines the necessary decoding level at inference time, requesting additional bits only when the current suitability is insufficient for the desired confidence level. Extensive experiments demonstrate that PICM-Net achieves efficient and adaptive progressive transmission while maintaining strong downstream classification performance. Our code is available at https://github.com/kjungwoo03/PICM-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
