Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

We propose a versatile deep image compression network based on Spatial Feature
Transform (SFT), which takes a source image and a corresponding quality map as inputs …

The strong demand of autonomous driving in the industry has led to vigorous interest in 3D
object detection and resulted in many excellent 3D object detection algorithms. However …

With the development of deep learning techniques, the combination of deep learning with
image compression has drawn lots of attention. Recently, learned image compression …

Recent deep image compression methods have achieved prominent progress by using
nonlinear modeling and powerful representation capabilities of neural networks. However …

Recently, learned image compression (LIC) has garnered increasing interest with its rapidly
improving performance surpassing conventional codecs. A key ingredient of LIC is a …

Instead of being observed by human, multimedia data are now more and more fed into
machines to perform different kinds of semantic analysis. One image may be analyzed …

Video coding that pursues the highest compression efficiency is the art of computing for rate-
distortion optimization. The optimization has been approached in different ways, exemplified …

In deep image compression, uniform quantization is applied to latent representations
obtained by using an auto-encoder architecture for reducing bits and entropy coding …

Recently, deep learning has been proven to be a promising approach in standard dynamic
range (SDR) image compression. However, due to the wide luminance distribution of high …