Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Datadriven Optimization of RowColumn Transforms for BlockBased Hybrid Video Compression
 Institute of Electrical and Electronics Engineers IEEE; IEEE Circuits and Systems Society: Picture Coding Symposium, PCS 2019 : Ningbo, China, 12  15 November 2019 Piscataway, NJ: IEEE, 2019 ISBN: 9781728147055 ISBN: 9781728147048 S.300304 
 Picture Coding Symposium (PCS) <34, 2019, Ningbo/China> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer HHI () 
Abstract
In stateoftheart video compression residual coding is done by transforming the prediction error signals into a less correlated representation and performing the quantization and entropy coding in the transform domain. For complexity reasons usually separable transforms are used. A more flexible transform structure is given by rowcolumn transforms, which apply a separate transform to each row and each column of a signal block. This paper describes a method for training such structured transforms by maximizing the data likelihood under a parameterized probabilistic model with a compelled structure. An explicit model is derived for the case of rowcolumn transforms and its efficiency is demonstrated in the application of video compression. It is shown that trained rowcolumn transforms achieve almost the same coding gain as unconstrained KLTs when applied as secondary transforms, while the encoder and decoder runtime are the same as in the separable transform case.