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 …

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 …

Lossy image compression always faces a tradeoff between rate-distortion performance and
compression/decompression speed. With the advent of neural image compression …

Deep image compression is formulated as a joint rate-distortion optimization problem using
an auto-encoder architecture. Latent representations obtained by an encoder are quantized …

In this work, we provide a detailed description on our submitted methods ANTxNN and
ANTxNN_SSIM to Workshop and Challenge on Learned Image Compression (CLIC) 2021 …

The ever-growing amount of images and videos conveyed over the Internet has a
substantial impact on climate change. This impact can be mitigated through the design of …

This paper analyses the deep image compression system that contains an encoder,
quantizer, entropy model and decoder optimized by a joint rate-distortion framework. The …