Signal Adaptive Methods To Optimize Prediction Signals in Video Coding

Signal Adaptive Methods To Optimize Prediction Signals in Video Coding introduces novel iterative filter methods for prediction signals based on state-of-the-art image processing methods.

The increasing demand for high resolution videos, together with limited transmission and memory capacity is still driving the research on high performance video compression codecs. As a core technique in state-of-the-art video codecs such as High Efficiency Video Coding (HEVC) a hybrid approach with block based architecture is used. The term hybrid refers to a combination of prediction from previous frames or adjacent blocks from the frame itself together with a transform coding of the resulting residual. Therefore, the quality of the prediction signal has a large influence on the efficiency of video codecs. Signal Adaptive Methods To Optimize Prediction Signals in Video Coding introduces novel iterative filter methods for prediction signals based on state-of-the-art image processing methods. It is shown that these filters significantly improve the rate-distortion performance of state-of-the-art hybrid video codecs while not imposing too much additional complexity.

Autorentext
Jennifer Rasch received her Diploma in Mathematics from the Humboldt University of Berlin, Germany in 2012. Her thesis was awarded as the best thesis in the field Numerics by the Deutsche Mathematiker Vereinigung (German Mathematical Union). Since 2014, she is as a research associate in the Video Coding & Analytics department at the Heinrich Hertz Institute in Berlin, Germany. She actively participated in the standardization process of the ITU-T Video Coding Experts Group since 2018. She defended her Ph.D. in Video Compression in 2019.