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 …

Today, many image coding scenarios do not have a human as final intended user, but rather
a machine fulfilling computer vision tasks on the decoded image. Thereby, the primary goal …

This paper presents a method allowing learned video encoders to apply arbitrary latent
refinement strategies to serve as RateDistortion Optimization (RDO) at the time of encoding …

Rate-distortion optimization (RDO) is responsible for large gains in image and video
compression. While RDO is a standard tool in traditional image and video coding, it is not yet …

Even though rate-distortion optimization is a crucial part of traditional image and video
compression, not many approaches exist which transfer this concept to end-to-end-trained …

We propose to employ a saliency-driven hierarchical neural image compression network for
a machine-to-machine communication scenario following the compress-then-analyze …

This paper introduces an online motion rate adaptation scheme for learned video
compression, with the aim of achieving content-adaptive coding on individual test …

This paper presents a method that enables arbitrary end-to-end Learning-based
image/video codecs to apply spatial rate allocation. At the frame-level, the forward pass of …

Wildfire detection is a time-critical application since it can be challenging to identify the
source of ignition in a short amount of time, which frequently causes the intensity of fire …

This paper provides a comprehensive study on features and performance of different ways
to incorporate neural networks into lifting-based wavelet-like transforms, within the context of …