通过简化空间操作、强化通道聚合，实现图像压缩的高速与高保真兼顾。
S2CFormer: Revisiting the RD-Latency Trade-off in Transformer-based Learned Image Compression
- 用轻量通道聚合替代复杂空间计算，提升效率
- 在Kodak等数据集上达到顶尖压缩性能与更快解码速度
- 适合追求高效高质图像压缩的工程与研究应用
基于Transformer的可学习图像压缩（LIC）存在解码延迟与率失真（R-D）性能之间的次优权衡。本文发现，基于前馈网络（FFN）的通道聚合模块起到关键作用，而以往研究过度关注复杂的空间操作。受此启发，提出S2CFormer通用架构：简化空间操作，强化通道操作，以突破原有权衡。设计了两个实例：S2C-Conv和S2C-Attention，均在保持顶级R-D性能的同时显著加快解码速度。进一步提出S2C-Hybrid，融合不同实例优势，在Kodak、Tecnick和CLIC Professional Validation数据集上全面超越现有方法，建立新基准。代码已开源。
原文摘要 · Abstract (English)
Transformer-based Learned Image Compression (LIC) suffers from a suboptimal trade-off between decoding latency and rate-distortion (R-D) performance. Moreover, the critical role of the FeedForward Network (FFN)-based channel aggregation module has been largely overlooked. Our research reveals that efficient channel aggregation-rather than complex and time-consuming spatial operations-is the key to achieving competitive LIC models. Based on this insight, we initiate the ``S2CFormer'' paradigm, a general architecture that simplifies spatial operations and enhances channel operations to overcome the previous trade-off. We present two instances of the S2CFormer: S2C-Conv, and S2C-Attention. Both models demonstrate state-of-the-art (SOTA) R-D performance and significantly faster decoding speed. Furthermore, we introduce S2C-Hybrid, an enhanced variant that maximizes the strengths of different S2CFormer instances to achieve a better performance-latency trade-off. This model outperforms all the existing methods on the Kodak, Tecnick, and CLIC Professional Validation datasets, setting a new benchmark for efficient and high-performance LIC. The code is at \href{https://github.com/YunuoChen/S2CFormer}{https://github.com/YunuoChen/S2CFormer}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。