An adaptive GOP structure selection for haar-like MCTF encoding based on mutual information
Multimedia Tools and Applications
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Motion-compensated temporal filtering (MCTF) is an innovative prediction scheme for video coding and it has become the core technology of the coming video coding standard, Scalable Video Coding. Since MCTF is important, this paper provides the system analysis of MCTF for hardware architecture design, including computational complexity, external memory bandwidth, and external memory size. The one-level MCTF is analyzed first, in which several frame-level data reuse schemes are proposed and the tradeoffs between external memory usages and on-chip memory size in these frame-level data reuse schemes are also discussed. Next, the analysis is extended to multilevel MCTF. The computational complexity of multilevel MCTF is close to that of traditional MC prediction with two reference frames. The memory bandwidth of multilevel MCTF depends on the frame-level data reuse scheme and performing the update stage or not. The external memory size is linearly proportional to the number of decomposition levels. Finally, a real-life test case is given to compare the system requirements between MCTF with various frame-level data reuse schemes and the prediction scheme of H.264/AVC