用卷积增强注意力机制，实现高效图像压缩
Convolutional Transformer-Based Image Compression
- 将卷积嵌入多头注意力，捕捉局部依赖
- 比特率与失真权衡优于现有CNN方法
- 计算量更低，适合实际部署
本文提出一种基于Transformer的端到端图像压缩新架构。该架构通过在多头注意力机制中引入卷积操作，有效捕捉令牌间的局部依赖关系，从而无需位置编码。实验表明，所提框架在比特率与失真权衡上优于当前最先进的基于CNN的方法，并达到与基于Transformer方法相当的性能，同时保持更低的计算复杂度。
原文摘要 · Abstract (English)
In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies between tokens, eliminating the need for positional encoding by integrating convolutional operations within the multi-head attention mechanism. We demonstrate through experiments that our proposed framework surpasses state-of-the-art CNN-based architectures in terms of the trade-off between bit-rate and distortion and achieves comparable results to transformer-based methods while maintaining lower computational complexity.
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