Our paper, “Spatially misaligned HEVC transcoding with computational-complexity scalability”, has been accepted for publication in the Journal of Visual Communication and Image Representation.
In control rooms, video walls display footage from multiple sources. Often, a composition of these sources is sent to other devices in a single video stream. To minimize the computational complexity of this composition process, information from the original bitstreams can be reused. However, in High Efficiency Video Coding (HEVC), simply copying the original encoding decisions is not compression efficient if the individual videos are spatially misaligned with the grid of coded blocks of the composition.
Our proposed HEVC-based transcoder reduces the computational complexity by predicting encoding decisions of misaligned sequences by using a trivial method or a more adaptive, computational-complexity scalable machine learning method.
Higher compression efficiency is observed when more alignment is preserved with the original block grid. Overall, the machine learning method achieves a higher compression efficiency than the trivial method. Both methods attain a complexity reduction of up to 82% compared to the reference software.