公开(公告)号：US10965948B1

公开(公告)日：2021-03-30

申请号：US16713910

申请日：2019-12-13

Applicant: Amazon Technologies, Inc.

Inventor： Srikar Appalaraju ,  Yash Patel ,  R. Manmatha

IPC: H04N19/20 ,  H04N19/91 ,  H04N19/33 ,  G06T9/00

Abstract: The present application relates to a multi-stage encoder/decoder system that provides image compression using hierarchical auto-regressive models and saliency-based masks. The multi-stage encoder/decoder system includes a first stage and a second stage of a trained image compression network, such that the second stage, based on the image compression performed by the first stage, identify certain redundancies that can be removed from the bit string to reduce the storage and bandwidth requirements. Additionally, by using saliency-based masks, distortions in different sections of the image can be weighted differently to further improve the image compression performance.

公开(公告)号：US10909728B1

公开(公告)日：2021-02-02

申请号：US16400900

申请日：2019-05-01

Applicant: Amazon Technologies, Inc.

Inventor： Srikar Appalaraju ,  R. Manmatha ,  Yash Patel

IPC: G06T9/00 ,  G06N20/10 ,  G06N3/08 ,  G06T3/40

Abstract: Techniques for learned lossy image compression are described. A system may perform image compression using an image compression model that includes an encoder to compress an image and a decoder to reconstruct the image. The encoder and the decoder are trained using machine learning techniques. After training, the encoder can encode image data to generate compressed image data and the decoder can decode compressed image data to generate reconstructed image data.