HANeRV: Hierarchically Adaptive Neural Representation for Video Compression.

Area of Science:

• Computer Vision
• Machine Learning
• Signal Processing

Background:

• Implicit Neural Representation (INR) methods offer superior video compression potential by optimizing global network parameters.
• Current INR methods use fixed architectures, limiting adaptability to dynamic video content and leading to suboptimal compression.
• Capturing dynamic variations within and between video sequences remains a challenge for existing INR approaches.

Purpose of the Study:

• To introduce Hierarchically Adaptive Neural Representation for Video Compression (HANeRV), an adaptive INR-based network.
• To enhance adaptability by optimizing network structures based on specific video sequence content.
• To improve compression performance by addressing limitations of fixed network architectures in current INR methods.

Main Methods:

• Proposed HANeRV, an innovative INR-based video compression network with adaptive structure optimization.
• Introduced dynamic architecture-level adjustment (DAA) to capture inter-sequence dynamics.
• Implemented dynamic frame-level adjustment (DFA) for intra-sequence frame dynamics and hierarchical structural adaptation (HSA) for spatial information.

Main Results:

• HANeRV achieved state-of-the-art performance among INR-based video compression methods.
• The proposed method surpassed the H.266/VVC (x266, medium preset) anchor on diverse datasets.
• Adaptive adjustments (DAA, DFA, HSA) effectively captured spatial and temporal dynamics for improved compression.

Conclusions:

• HANeRV demonstrates superior adaptability and compression efficiency compared to existing INR methods.
• The adaptive approach overcomes limitations of fixed architectures, enabling better handling of dynamic video content.
• HANeRV represents a significant advancement in neural representation for video compression, achieving competitive results against established standards.