53 citations · 123 across the 18 of their papers we have counts for
8 papers · 1 filter
Spatially-Adaptive Learning-Based Image Compression with Hierarchical Multi-Scale Latent Spaces
Fabian Brand, Alexander Kopte, Kristian Fischer +1
Adaptive block partitioning is responsible for large gains in current image and video compression systems. This method is able to compress large stationary image areas with only a…
Conditional Residual Coding: A Remedy for Bottleneck Problems in Conditional Inter Frame Coding
Fabian Brand, Jürgen Seiler, André Kaup
Conditional coding is a new video coding paradigm enabled by neural-network-based compression. It can be shown that conditional coding is in theory better than the traditional resi…
Processing Energy Modeling for Neural Network Based Image Compression
Christian Herglotz, Fabian Brand, Andy Regensky +2
Nowadays, the compression performance of neural-networkbased image compression algorithms outperforms state-of-the-art compression approaches such as JPEG or HEIC-based image compr…
Learned Wavelet Video Coding using Motion Compensated Temporal Filtering
Anna Meyer, Fabian Brand, André Kaup
We present an end-to-end trainable wavelet video coder based on motion-compensated temporal filtering (MCTF). Thereby, we introduce a different coding scheme for learned video comp…
Multiscale Augmented Normalizing Flows for Image Compression
Marc Windsheimer, Fabian Brand, André Kaup
Most learning-based image compression methods lack efficiency for high image quality due to their non-invertible design. The decoding function of the frequently applied compressive…
The Bjøntegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong
Christian Herglotz, Hannah Och, Anna Meyer +8
In this paper, we provide an in-depth assessment on the Bjøntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics…
Saliency-Driven Hierarchical Learned Image Coding for Machines
Kristian Fischer, Fabian Brand, Christian Blum +1
We propose to employ a saliency-driven hierarchical neural image compression network for a machine-to-machine communication scenario following the compress-then-analyze paradigm. B…
Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image Coding
Kristian Fischer, Fabian Brand, Christian Herglotz +1
Today, visual data is often analyzed by a neural network without any human being involved, which demands for specialized codecs. For standard-compliant codec adaptations towards ce…