Av1 Motion Vector Fidelity Enables Efficient Optical Flow Estimation As a High-Quality Substitute

Motion estimation forms a crucial, yet computationally demanding, step in many computer vision tasks, and researchers continually seek ways to improve its efficiency. Julien Zouein, Vibhoothi Vibhoothi, and Anil Kokaram, all from Trinity College Dublin, investigate a novel approach by examining the potential of motion vectors generated during video compression. Their work demonstrates that motion vectors extracted from the AV1 video codec offer a surprisingly accurate and efficient alternative to traditional flow estimation methods. By rigorously comparing AV1 and HEVC motion vectors against established ground-truth data, the team establishes their fidelity and identifies optimal encoder settings, and importantly, they show that leveraging these vectors as a starting point for advanced flow algorithms, such as RAFT, achieves a four-fold increase in processing speed with minimal impact on accuracy, opening up new possibilities for real-time motion-aware applications.

can serve as a high-quality and computationally efficient substitute for traditional optical flow, a critical but often resource-intensive component in many computer vision pipelines. This work establishes the fidelity of motion vectors from AV1 and HEVC codecs through detailed comparison against ground-truth optical flow, demonstrating the impact of encoder settings on motion estimation accuracy and recommending optimal configurations. Furthermore, the research reveals that utilising these extracted AV1 motion vectors as a “warm-start” for RAFT, a state-of-the-art deep learning-based optical flow method, significantly reduces the time required for convergence.

Motion Vectors Guide Deep Optical Flow Estimation

This research paper explores how motion vectors (MVs) extracted from compressed video, specifically AV1 encoded video, can improve the quality of optical flow estimation. The core idea is that these readily available MVs can guide deep learning-based optical flow algorithms, leading to faster and more accurate results, particularly in areas with fine detail. The study addresses the challenge of computationally expensive and often inaccurate optical flow estimation in complex scenes by proposing a solution that leverages existing information within compressed video. The researchers investigated how to effectively integrate these MVs into deep learning models for optical flow estimation, using the SPRING dataset, a high-resolution benchmark, to evaluate their approach.

Key findings demonstrate that motion vectors provide a valuable prior for optical flow estimation, especially in detailed regions, leading to both faster processing and improved accuracy. The SPRING dataset is highlighted as a valuable resource for evaluating optical flow algorithms. In essence, the paper demonstrates that readily available information within compressed video can be effectively harnessed to enhance the performance of optical flow estimation, offering a promising avenue for real-time and accurate vision processing. Potential applications include improved scene understanding and obstacle detection for autonomous driving, more accurate tracking of objects and events in video surveillance, more realistic motion estimation for video editing and special effects, and more accurate depth estimation from video sequences.

AV1 Motion Vectors Accelerate Optical Flow Estimation

This work demonstrates a novel approach to accelerating motion estimation by leveraging motion vectors already embedded within compressed AV1 video streams. Researchers discovered that these motion vectors can effectively substitute for traditionally computed flow, a computationally intensive step in many computer vision applications. The study meticulously compared motion vectors from both AV1 and HEVC codecs against ground-truth flow data, establishing their fidelity and identifying optimal encoder settings for maximum accuracy. The core achievement lies in utilising extracted AV1 motion vectors as a “warm-start” for RAFT, a state-of-the-art deep learning method for optical flow estimation.

Experiments revealed a significant four-fold speedup in processing time with only a minor trade-off in end-point error. This acceleration is achieved by providing RAFT with a pre-existing motion field, allowing it to converge much faster than starting from scratch. The process begins by extracting sparse motion vectors from the AV1 bitstream and normalising them to reference the immediately preceding frame. Missing motion data, common in compressed video, is intelligently inferred using bidirectional motion vector completion. To create a dense motion field at full frame resolution, the sparse vectors are upsampled using a zero-order hold method.

The resulting field is then refined using RAFT. Researchers even extended RAFT’s training to specifically incorporate this “warm-start” initialisation, further enhancing its performance with compressed video data. The results confirm that motion vectors from AV1 offer a practical and efficient means of accelerating motion-aware vision applications, opening new possibilities for real-time processing and reduced computational demands.

AV1 Motion Vectors Accelerate Optical Flow Estimation

This research demonstrates that motion vectors extracted from video encoded with the AV1 codec can serve as a high-quality and computationally efficient substitute for traditional flow estimation techniques. The team rigorously compared motion vectors from both AV1 and HEVC codecs against ground-truth flow data, establishing their fidelity and identifying optimal encoder settings for accurate motion representation. Results indicate that AV1 motion vectors, particularly when used to initialise a state-of-the-art deep learning method, RAFT, significantly reduce computation time, achieving a four-fold speedup, with only a minor reduction in accuracy. These findings underscore the potential of reusing motion information from compressed video for a wide range of applications requiring motion awareness, such as pixel enhancement and frame interpolation. The study also highlights that libaom, an AV1 encoder, delivers comparable motion accuracy to HEVC while offering improved perceptual video quality. While the research demonstrates strong performance across various video sequences, the authors acknowledge that performance varies depending on the complexity of motion within the video, with larger and more complex motions presenting greater challenges.