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.

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。该方法包括几个新颖的组件，其中包括充分的感知相似度度量、图像中的显着性和分层自回归模型。该模型不仅生成视觉效果更好的图像，而且在后续计算机视觉任务（如目标检测和分割）中也具有优越的性能。