Abstract
Sample Adaptive Offset is a new adopted technology by HEVC in recent years, which improves the visual quality of reconstructed videos significantly. However, there are two problems in current SAO technology. The first is that the statistic phase needs to traverse each pixel to collect relevant information. The other problem is that the complexity of SAO is too high for SAO mode decision stage, which needs to be performed on each CTU. To solve these problems, we proposed a fast SAO algorithm in HEVC encoder. We explore the correlation of SAO type among neighboring CTUs, and then utilize this spatial information to reduce the complexity of SAO. Experimental results demonstrate that our proposed method can achieve about 62%, 80% and 75% SAO encoding time saving on average in AI, RA, and LDB test condition compared with HM16.0 respectively. At the same time, the proposed method just causes negligible compression performance loss.
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Acknowledgement
This work was supported in part by the National Science Foundation of China (NSFC) under grants 61472101 and 61631017, the National High Technology Research and Development Program of China (863 Program 2015AA015903), and the Major State Basic Research Development Program of China (973 Program 2015CB351804).
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Sun, C., Wang, Y., Fan, X., Zhao, D. (2018). A New Fast Algorithm for Sample Adaptive Offset. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_38
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DOI: https://doi.org/10.1007/978-3-319-77383-4_38
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