Asymmetric Bilateral Motion Estimation for Video Frame Interpolation (2108.06815v1)

Abstract: We propose a novel video frame interpolation algorithm based on asymmetric bilateral motion estimation (ABME), which synthesizes an intermediate frame between two input frames. First, we predict symmetric bilateral motion fields to interpolate an anchor frame. Second, we estimate asymmetric bilateral motions fields from the anchor frame to the input frames. Third, we use the asymmetric fields to warp the input frames backward and reconstruct the intermediate frame. Last, to refine the intermediate frame, we develop a new synthesis network that generates a set of dynamic filters and a residual frame using local and global information. Experimental results show that the proposed algorithm achieves excellent performance on various datasets. The source codes and pretrained models are available at https://github.com/JunHeum/ABME.

• The paper introduces an innovative asymmetric bilateral motion estimation algorithm to improve video frame interpolation accuracy.
• It refines symmetric motion fields using a synthesis network with FilterNet and RefineNet for enhanced non-linear motion representation.
• Experimental evaluations on UCF101, Vimeo90K, and SNU-FILM show significant PSNR and SSIM improvements over prior methods.

Asymmetric Bilateral Motion Estimation for Video Frame Interpolation: A Comprehensive Review

The paper presents an innovative approach to video frame interpolation via Asymmetric Bilateral Motion Estimation (ABME). This research focuses on addressing the inherent limitations of existing interpolation methods, particularly the symmetric motion constraints that often lead to inaccuracies in regions with complex or non-linear motion and occlusions.

Research Highlights

The paper introduces an Asymmetric Bilateral Motion Estimation algorithm to enhance video frame interpolation. The proposed method transitions from symmetric bilateral motion fields to asymmetric ones, thereby achieving more accurate motion representation. The approach can be outlined in the following key steps:

1. Bilateral Motion Field Prediction and Refinement:
   • Initially, symmetric bilateral motion fields are estimated to interpolate a temporary intermediate frame, referred to as an anchor frame.
   • The asymmetric motion fields are derived from these symmetric fields by relaxing linear constraints, allowing for more accurate representation, especially in non-linear motion regions.
2. Use of Synthesis Network:
   • A sophisticated synthesis network is developed, incorporating a FilterNet and a RefineNet. FilterNet is responsible for generating dynamic filters based on local information, while RefineNet leverages these filters along with global context for residual frame reconstruction.
3. Experimental Validation:
   • The paper quantitatively evaluates the proposed ABME framework using standard benchmarks such as UCF101, Vimeo90K, and SNU-FILM datasets, demonstrating superior performance over existing methods, particularly in challenging scenarios with fast-moving objects and substantial occlusions.
   • ABME yields notable improvements in PSNR and SSIM scores, outshining state-of-the-art techniques like DAIN, CAIN, and BMBC across various datasets.

Implications and Future Directions

The implications of the ABME algorithm are significant for both theoretical and practical domains:

• Theoretical Contributions:
  • The introduction of asymmetric motion estimation challenges the conventional motion symmetry assumption, providing a new perspective that can be extended to other applications in video processing where accurate motion representation is crucial.
• Practical Applications:
  • This advancement in interpolation algorithms is pivotal for applications requiring high-quality video playback, such as video gaming, virtual reality, and slow-motion video creation.

Future developments could involve integrating ABME into real-time systems where computational efficiency remains a concern. Moreover, exploring its compatibility with other types of motion analysis, such as depth estimation or 3D reconstruction, could further enhance the utility and adaptability of the framework.

In conclusion, the proposed ABME algorithm significantly advances the field of video frame interpolation, providing a robust solution to the challenges posed by complex motion patterns and occlusions in digital videos. With its superior performance and potential for further extension, ABME sets a new standard for interpolation techniques in the era of digital video enhancement.