A Parallel and Pipelined Execution of H.264/AVC Intra Prediction
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Interactive presentation: An efficient hardware architecture for H.264 intra prediction algorithm
Proceedings of the conference on Design, automation and test in Europe
3-tier dynamically adaptive power-aware motion estimator for h.264/AVC video encoding
Proceedings of the 13th international symposium on Low power electronics and design
Journal of Signal Processing Systems
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Analysis, fast algorithm, and VLSI architecture design for H.264/AVC intra frame coder
IEEE Transactions on Circuits and Systems for Video Technology
Parallel fast inter mode decision for H.264/AVC encoding
Journal of Visual Communication and Image Representation
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The H.264/AVC Intra Frame Codec (i.e. all frames are coded as I-frames) targets high-resolution/high-end encoding applications (e.g. digital cinema and high quality archiving etc.), providing much better compression efficiency at lower computational complexity compared to MJPEG2000. Moreover, in case of video coding of very high motion scenes, the number of Intra Macroblocks is dominant. Intra Prediction is a compute intensive and memory-critical part that consumes 80% of the computation time of the entire Intra Compression process when executing the H.264 encoder on MIPS processor [13]. We therefore present a novel hardware for H.264 Intra Prediction that processes all the prediction modes in parallel inside one integrated module (i.e. mode-level parallelism) enabling us to exploit the full space of optimization. It exhibits a group-based write-back scheme to reduce the memory transfers in order to facilitate the fast mode-decision schemes. Our Luma 4x4 hardware is 3.6x, 5.2x, and 5.5x faster than state-of-the-art approaches [13], QS0 [14], and [15], respectively. Our results show that processing Luma 16x16, Chroma 8x8, and Luma 4x4 with the proposed approach is 7.2x, 6.5x, and 1.8x faster (while giving an energy saving of 60%, 80%, and 74%) when compared with Dedicated Module Approach [13] (each prediction mode is processed with its independent hardware module i.e. a typical ASIC style for Intra Prediction). We get an area saving of 58% for Luma 4x4 hardware.