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Optimal CTU-Level Rate Control Model for HEVC Based on Deep Convolutional Features
2020
IEEE Access
This paper proposes an optimal rate control model based on deep neural network (DNN) features to improve the coding tree unit (CTU)-level rate control in high-efficiency video coding for conversational videos. The proposed algorithm extracts high-level features from the original and previously reconstructed CTU blocks based on a predefined DNN model of the visual geometry group (VGG-16) network. Then, the correlation of the high-level feature and quantization parameter (QP) values of previously
doi:10.1109/access.2020.3022408
fatcat:ji33xcq2areb7gjj6c4xkntix4