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Found 8 papers, 3 papers with code
Learned Video Compression for YUV 4:2:0 Content Using Flow-based Conditional Inter-frame Coding
To adapt our codec to YUV 4:2:0 content, we adopt a simple strategy of using space-to-depth and depth-to-space conversions.
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265
Recently, the dual-critic design is proposed to update the actor by alternating the rate and distortion critics.
B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
This work introduces a B-frame coding framework, termed B-CANF, that exploits conditional augmented normalizing flows for B-frame coding.
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
CANF-VC represents a new attempt that leverages the conditional ANF to learn a video generative model for conditional inter-frame coding.
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization
More recently, the dual-critic design is proposed to update the actor network by alternating the rate and distortion critics.
ANFIC: Image Compression Using Augmented Normalizing Flows
This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF).
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265
This paper introduces a dual-critic reinforcement learning (RL) framework to address the problem of frame-level bit allocation in HEVC/H. 265.
SME-Net: Sparse Motion Estimation for Parametric Video Prediction Through Reinforcement Learning
Inspired by the success of sparse motion-based prediction for video compression, we propose a parametric video prediction on a sparse motion field composed of few critical pixels and their motion vectors.
