Macroblock sampling and mode ranking for complexity scalability in mobile H.264 video coding

We propose a framework for complexity scalability in H.264. The prediction is constrained so that only a subset of prediction modes are tested. The test subset is found by ranking the most “popular” modes (those the are most often picked as best) and selecting the modes that maximize their expected occurrence frequency given a complexity constraint. Ranking is performed by selecting a small set of macroblocks (the sampling population), for which all modes are tested. The remaining macroblocks only test the available dominant modes. The modes in the sampling population of a frame are used to process the next frame. Results are shown to verify the performance of the proposed method, which reveals sizeable complexity savings at small penalties.

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