Machine Learning Based Symbol Probability Distribution Prediction For Entropy Coding In Av1

Entropy coding is a lossless data compression technique that is widely applied in video codecs to encode syntax elements into bitstreams. Efficient entropy coding requires accurate prediction of the probability distribution of the encoded symbols. In AV1, multi-symbol arithmetic coding is adopted. The symbol probability is derived with handcrafted context models and lookup tables that store the predicted probabilities corresponding to different entropy contexts. The lookup table based scheme has some fundamental deficiencies. The entropy context features have to be discrete so that they can be used to index the lookup tables. To reduce the size of the lookup table, the number of contexts cannot be very large. Moreover, the probability distributions stored in the lookup tables are maintained separately without taking their correlations into consideration. In this paper, we propose a machine learning based scheme that achieves more accurate symbol probability prediction for entropy coding. The proposed approach is implemented in AV1 for the entropy coding of intra prediction modes. Experimental results demonstrate that it can improve the efficiency of entropy coding significantly.