Video compression using context-based adaptive arithmetic coding

A new entropy coding scheme for video compression is presented. Context models are utilized for efficient prediction of the coding symbols. A novel binary adaptive arithmetic coding technique is employed to match the conditional entropy of the coding symbols given the context model estimates. The adaptation is also employed to keep track of non-stationary symbol statistics. Our new approach has been integrated into the current ITU-T H.26L test model (TML) to demonstrate the performance gain. By using our new entropy coding scheme instead of the current one variable length code approach of the current TML, large bit-rate savings up to 32% can be achieved. As a remarkable outcome of our experiments, we observed that high gains are reached not only at high bit-rates, but also at very low rates.