Reinforcement Learning for HEVC/H.265 Frame-level Bit Allocation

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 depend...

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Bibliographic Details
Main Authors: Chen, Lian-Ching, Hu, Jun-Hao, Peng, Wen-Hsiao
Format: Conference Proceeding
Language:English
Subjects:
Bit rate
Complexity theory
deep reinforcement learning
Distortion
Encoding
Feature extraction
frame-level bit allocation
HEVC/H.265
Reinforcement learning
Training
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Summary: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 paper 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 HM-16.15, our method shows better PSNR performance while having smaller bit rate fluctuations.
ISSN:2165-3577
DOI:10.1109/ICDSP.2018.8631541