Learning-Based Rate Control for High Efficiency Video Coding

Area of Science:

• Video Compression Technology
• Machine Learning Applications
• Digital Signal Processing

Background:

• High Efficiency Video Coding (HEVC) offers superior compression over H.264/AVC.
• Current HEVC rate control methods suffer from fixed parameter initialization, leading to inaccurate bit allocation predictions.
• This inaccuracy impacts overall video quality and efficient bit usage.

Purpose of the Study:

• To develop an advanced rate control mechanism for HEVC.
• To enhance coding efficiency and video quality through intelligent parameter mapping.
• To address the limitations of fixed initialization in existing HEVC rate control algorithms.

Main Methods:

• A learning-based mapping framework is proposed, integrating spatial and temporal coding strategies.
• Particle Swarm Optimization (PSO) is employed for optimizing rate control parameters at the Coding Tree Unit (CTU) level.
• Semantic residual information is introduced for picture-level temporal coding to refine bit allocation.

Main Results:

• The proposed algorithm demonstrates effectiveness in HEVC rate control.
• It achieves an average improvement of 0.19 dB in quality over state-of-the-art methods.
• Up to 0.41 dB improvement was observed for low-delay P coding structures.

Conclusions:

• The learning-based rate control method significantly enhances HEVC performance.
• Accurate bit allocation and improved video quality are achieved.
• The proposed approach offers a robust solution for advanced video coding challenges.