Compression for machines is an emerging field, where inputs are encoded while optimizing
the performance of down-stream automated analysis. In scalable coding for humans and …

In this paper we present aa deep generative model for lossy video compression. We employ
a model that consists of a 3D autoencoder with a discrete latent space and an …

Most existing image compression approaches perform transform coding in the pixel space to
reduce its spatial redundancy. However they encounter difficulties in achieving both high …

Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing
category of Internet traffic is machine-to-machine communication. In particular, machine-to …

We present an end-to-end trainable image compression frameworkforlowbit-
rateimagecompression. Ourmethod is based on variational autoencoder, which consists of a …

An increasing share of image and video content is analyzed by machines rather than viewed
by humans, and therefore it becomes relevant to optimize codecs for such applications …

Video coding, a process of compressing and decompressing digital video content, has
traditionally been optimized for human visual systems by reducing its size while maintaining …

In recent years, there has been a sharp increase in transmission of images to remote servers
specifically for the purpose of computer vision. In many applications, such as surveillance …

Machine vision tasks such as object detection and instance segmentation are becoming
more and more popular these days due to the quickly increasing performance of deep …

In this paper, we provide the description of our approach designed for participating the
CVPR 2018 Challenge on Learned Image Compression (CLIC). Our approach is a hybrid …