Data-parallel intra decoding for block-based image and video coding on massively parallel architectures

With the increasing number of processor cores available in modern computing architectures, task or data parallelism is required to maximally exploit the available hardware and achieve optimal processing speed. Current state-of-the-art data-parallel processing methods for decoding image and video bitstreams are limited in parallelism by dependencies introduced by the coding tools and the number of synchronization points introduced by these dependencies, only allowing task or coarse-grain data parallelism. In particular, entropy decoding and data prediction are bottleneck coding tools for parallel image and video decoding. We propose a new data-parallel processing scheme for block-based intra sample and coefficient prediction that allows fine-grain parallelism and is suitable for integration in current and future state-of-the-art image and video codecs. Our prediction scheme enables maximum concurrency, independent of slice or tile configuration, while minimizing synchronization points. This paper describes our data-parallel processing scheme for one- and two-dimensional prediction and investigates its application to block-based image and video codecs using JPEG XR and H.264/AVC Intra as a starting point. We show how our scheme enables faster decoding than the state-of-the-art wavefront method with speedup factors of up to 21.5 and 7.9 for JPEG XR and H.264/AVC Intra coding tools respectively. Using the H.264/AVC Intra coding tool, we discuss the requirements of the algorithm and the impact on decoded image quality when these requirements are not met. Finally, we discuss the impact on coding rate in order to allow for optimal parallel intra decoding.