This paper presents a fast context-based adaptive variable-length decoding (CAVLD) method of H.264/AVC with a very long instruction word-based bitstream processing unit (BsPU) designed for entropy decoding of multiple video formats. A new table mapping algorithm for the coeff_token, level, and run_before syntax elements of the quantized transform coefficients is proposed, and many branch operations are removed by utilizing several designated instructions in the BsPU. By applying designated instructions and the proposed table mapping algorithm to CAVLD, we found that the proposed fast CAVLD method achieves an increase of approximately 47% in the decoding speed and a reduction of approximately 59% in memory requirements for the table mapping.
In this paper, we propose a complexity-based parallelization method of the sample adaptive offset (SAO) algorithm which is one of HEVC in-loop filters. The SAO algorithm can be regarded as region-based process and the regions are obtained and represented with a quad-tree scheme. A offset to minimize a reconstruction error is sent for each partitioned region. The SAO of the HEVC can be parallelized in data-level. However, because the sizes and complexities of the SAO regions are not regular, workload imbalance occurs with multi-core platform. In this paper, we propose a LCU-based SAO algorithm and a complexity prediction algorithm for each LCU. With the proposed complexity-based LCU processing, we found that the proposed algorithm is faster than the sequential implementation by a factor of 2.38 times. In addition, the proposed algorithm is faster than regular parallel implementation SAO by 21%.
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