Abstract
The upcoming Versatile Video Coding (VVC) standard has improved the intra coding efficiency by introducing a great number of intra prediction modes. The cost on signaling the prediction modes is heavy. To reduce the mode coding overhead as well as to leverage multi-hypothesis prediction, we propose an intra coding method on top of VVC. For each block, we use a trained neural network to predict the probability distribution of all possible intra prediction modes. The predicted probabilities are used twofold. On the one hand, the probability values are used as weights to fuse the prediction signals generated by multiple modes, leading to a new prediction signal. On the other, the probability values are used by an advanced arithmetic coding instead of the context adaptive binary arithmetic coding. With the proposed method, the intra coding efficiency is improved by more than 1% averagely upon the VVC test model.
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Acknowledgments
This work was supported by the Natural Science Foundation of China under Grants 62022075, 62036005, 62021001, and by the University Synergy Innovation Program of Anhui Province under No. GXXT-2019-025.
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Zhang, Z., Ma, C., Liu, D., Li, L., Wu, F. (2021). Improving VVC Intra Coding via Probability Estimation and Fusion of Multiple Prediction Modes. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_54
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DOI: https://doi.org/10.1007/978-3-030-87355-4_54
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