论文标题
语义辅助图像压缩
Semantic-assisted image compression
论文作者
论文摘要
传统的图像压缩方法通常针对像素级的一致性，同时忽略下游AI任务的性能。要解决此问题，本文提出了一种语义辅助图像压缩方法（SAIC），该方法可以保持语义级别的一致性，以维持下游AI的高性能，以启用下游AI任务的高性能。特别是，使用基于梯度的语义权重机制（GSW）测量语义级别的损失。 GSW直接考虑下游AI任务的感知结果。然后，本文提出了一个语义级失真评估度量，以量化压缩过程中保留的语义信息量。实验结果表明，与传统的基于深度学习的方法相比，提出的SAIC方法可以保留更多的语义级信息，并以相同的压缩比的传统深度学习方法和先进的感知方法相比，在下游AI任务中获得更好的性能。
Conventional image compression methods typically aim at pixel-level consistency while ignoring the performance of downstream AI tasks.To solve this problem, this paper proposes a Semantic-Assisted Image Compression method (SAIC), which can maintain semantic-level consistency to enable high performance of downstream AI tasks.To this end, we train the compression network using semantic-level loss function. In particular, semantic-level loss is measured using gradient-based semantic weights mechanism (GSW). GSW directly consider downstream AI tasks' perceptual results. Then, this paper proposes a semantic-level distortion evaluation metric to quantify the amount of semantic information retained during the compression process. Experimental results show that the proposed SAIC method can retain more semantic-level information and achieve better performance of downstream AI tasks compared to the traditional deep learning-based method and the advanced perceptual method at the same compression ratio.
