The Video Coding Technologies Group (VCT) is oriented towards the research and development of next-generation video coding and compression technologies. A lot of our recent research work is contained in our response to the Call for Proposals on video compression. The main new compression tools that we developed are described in the paper  in the reference below.
We have developed partitioning methods for video coding that allow a highly flexible adaptation to the signal characteristics.
Transform Coefficient Quantization and Coding
We have designed a transform coding scheme that combines trellis-coded quantization with an improved entropy coding of quantization indices.
Machine Learning Based Compression
We have applied methods from machine learning for a desing of new compression tools.
Advanced Prediction and Reconstruction Methods
We developed new tools to improve inter- and intra-picture prediction and reconstruction in hybrid video codecs.
Perceptually Optimized Video Coding
We have designed an encoder control that is based on a perceptually motivated distortion measure.
Neural Network Coding
Efficient compression of neural networks has become an important topic for international standardization bodies, like ISO/IEC MPEG in order to provide billions of people with standardized neural network coding tools for fast and interoperable deep learning Solutions.
- J. Pfaff, H. Schwarz, D. Marpe, B. Bross, S. De-Luxán-Hernández, P. Helle, C. R. Helmrich, T. Hinz, W. Q. Lim, J. Ma, T. Nguyen, J. Rasch, M. Schäfer, M. Siekmann, G. Venugopal, A. Wieckowski, M. Winken, and T. Wiegand. Video Compression Using Generalized Binary Partitioning, Trellis Coded Quantization, Perceptually Optimized Encoding and Advanced Techniques for Prediction and Transform Codi. IEEE Transactions on Circuits and Systems For Video Technology, 2019. DOI: 10.1109/TCSVT.2019.2945918