通过优选扩散过程中的种子，提升人眼感知图像质量。
Seed Selection for Human-Oriented Image Reconstruction via Guided Diffusion
- 从多个候选种子中选最优，基于逆扩散早期中间输出
- 在不增加码率前提下，多指标超越单种子基线
- 适合追求图像感知质量的低码率重建场景
传统的人机共存图像编码方法需额外传输信息以实现可扩展性。最近一种基于扩散的方法无需额外比特率，即可从机器优化图像生成人眼优化图像。然而，该方法仅使用单一随机种子，可能导致图像质量不佳。本文提出一种种子选择方法，从多个候选种子中筛选最优者，以提升图像质量且不增加比特率。为降低计算开销，选择基于逆扩散过程早期步骤的中间输出完成。实验结果表明，所提方法在多个评估指标上均优于使用单一随机种子的基线方法。
原文摘要 · Abstract (English)
Conventional methods for scalable image coding for humans and machines require the transmission of additional information to achieve scalability. A recent diffusion-based approach avoids this by generating human-oriented images from machine-oriented images without extra bitrate. However, it utilizes a single random seed, which may lead to suboptimal image quality. In this paper, we propose a seed selection method that identifies the optimal seed from multiple candidates to improve image quality without increasing the bitrate. To reduce the computational cost, selection is performed based on intermediate outputs obtained from early steps of the reverse diffusion process. Experimental results demonstrate that our proposed method outperforms the baseline, which uses a single random seed without selection, across multiple evaluation metrics.
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