Improving Coding Efficiency of Massive Parallel Intra Prediction Using Alternative References

Exploring massive parallelism is a common strategy to mitigate the processing time of modern video encoding standards. Nonetheless, data dependencies challenge parallelism exploitation, especially during intra prediction, where the reconstructed adjacent blocks are used as references. Some works use the original frame samples as references to decouple adjacent blocks and allow parallelism. Still, the original samples are static and cannot model the nuances of different bitrates. In this context, this work seeks to improve the coding efficiency of parallel intra prediction implementations by using alternative reference samples based on low-pass filters that better represent the nuances of different bitrates for any partitioning structure. Variations in multiple aspects of the filters are considered, such as their dimension and also the precision and distribution of their coefficients. Experimental evaluations assessed the similarity of such alternative samples when compared to the regular ones, in addition to their impacts on coding efficiency and the processing overhead required to obtain such samples. The results from such experiments demonstrate that the alternative references improve coding efficiency when compared to the original samples, especially at lower bitrates. Furthermore, the additional filtering stage poses negligible timing overhead in most computing systems.