论文标题
显着驱动的感知图像压缩
Saliency Driven Perceptual Image Compression
论文作者
论文摘要
本文提出了一种新的端到端可训练模型，用于有损图像压缩，其中包括几个新型组件。该方法包含1）足够的感知相似性度量； 2）图像中的显着性； 3）分层自动回归模型。本文表明，诸如MS-SSIM和PSNR之类的普遍使用的评估指标不足以判断图像压缩技术的性能，因为它们与人类对相似性的看法不符。另外，提出了一个新的度量标准，该指标是在特定于图像压缩的感知相似性数据上学习的。提出的压缩模型结合了显着区域，并对所提出的感知相似性度量进行了优化。该模型不仅会在视觉上生成更好的图像，而且还可以为随后的计算机视觉任务任务（例如对象检测和分割）提供卓越的性能，与现有的工程或学习的压缩技术相比。
This paper proposes a new end-to-end trainable model for lossy image compression, which includes several novel components. The method incorporates 1) an adequate perceptual similarity metric; 2) saliency in the images; 3) a hierarchical auto-regressive model. This paper demonstrates that the popularly used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging the performance of image compression techniques as they do not align with the human perception of similarity. Alternatively, a new metric is proposed, which is learned on perceptual similarity data specific to image compression. The proposed compression model incorporates the salient regions and optimizes on the proposed perceptual similarity metric. The model not only generates images which are visually better but also gives superior performance for subsequent computer vision tasks such as object detection and segmentation when compared to existing engineered or learned compression techniques.
