Content Adaptive based Motion Alignment Framework for Learned Video Compression

2025-12-14

AI Summary

Existing end-to-end video compression methods lack content adaptivity, leading to inaccurate motion compensation and imprecise alignment. To address this, we propose a content-adaptive motion alignment framework with three key innovations: (1) an optical-flow-guided coarse-to-fine deformable warping module augmented with mask modulation to enhance deformation accuracy; (2) a reference-frame-quality-driven hierarchical distortion-weighting strategy to improve reconstruction fidelity; and (3) a motion-magnitude-aware, training-free dynamic frame downsampling scheme for resolution-adaptive optimization. All components are jointly optimized in an end-to-end manner. Evaluated on standard benchmarks, our method achieves a 24.95% BD-rate reduction (in PSNR) over the DCVC-TCM baseline, significantly outperforming both our reimplementation of DCVC-DC and the HEVC reference software HM-16.25.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Recent advances in end-to-end video compression have shown promising results owing to their unified end-to-end learning optimization. However, such generalized frameworks often lack content-specific adaptation, leading to suboptimal compression performance. To address this, this paper proposes a content adaptive based motion alignment framework that improves performance by adapting encoding strategies to diverse content characteristics. Specifically, we first introduce a two-stage flow-guided deformable warping mechanism that refines motion compensation with coarse-to-fine offset prediction and mask modulation, enabling precise feature alignment. Second, we propose a multi-reference quality aware strategy that adjusts distortion weights based on reference quality, and applies it to hierarchical training to reduce error propagation. Third, we integrate a training-free module that downsamples frames by motion magnitude and resolution to obtain smooth motion estimation. Experimental results on standard test datasets demonstrate that our framework CAMA achieves significant improvements over state-of-the-art Neural Video Compression models, achieving a 24.95% BD-rate (PSNR) savings over our baseline model DCVC-TCM, while also outperforming reproduced DCVC-DC and traditional codec HM-16.25.

Problem

• Improves video compression by adapting encoding to content characteristics
• Enhances motion compensation with flow-guided deformable warping mechanism
• Reduces error propagation via multi-reference quality aware strategy

Innovation

• Two-stage flow-guided deformable warping for precise motion compensation
• Multi-reference quality aware strategy to reduce error propagation
• Training-free downsampling module for smooth motion estimation