| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 107 === Frame-level bit allocation is crucial to video rate control. The problem is often cast as minimizing the distortions of a group of video frames subjective to a rate constraint. When these video frames are related through inter-frame prediction, the bit allocation for different frames exhibits dependency. To address such dependency, this thesis introduces reinforcement learning. We first consider frame-level texture complexity and bit balance as a state signal, define the bit allocation for each frame as an action, and compute the negative frame-level distortion as an immediate reward signal. We then train a neural network to be our agent, which observes the state to allocate bits to each frame in order to maximize cumulative reward. As compared to the rate control scheme in x265-2.7, our method has smaller bit rate fluctuations. However, the coding performance of our model still has room for improvement.
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