AV1 Motion Vectors: Concepts & Methods

• AV1 motion vectors are block-based displacement parameters crucial for inter-frame prediction and rate–distortion optimization.
• Estimation methods, including full block matching and neural network predictors, achieve sub-pixel accuracy and bitrate savings.
• Extracted from compressed video, these vectors serve as efficient surrogates for dense optical flow in computer vision tasks.

AV1 motion vectors are block-based displacement parameters embedded in the AV1 video codec bitstream, representing temporal correspondence between regions (blocks) of consecutive frames. Serving as a core mechanism for inter-frame prediction and efficient video compression, AV1 motion vectors enable predictive referencing, drive rate–distortion optimization, and underlie various downstream computer vision tasks when extracted post hoc. Their manifestation, estimation, encoding, and application have evolved with AV1, reflecting both classic algorithmic constructs and emerging neural or data-driven models.

1. Role and Representation of Motion Vectors in AV1

Motion vectors (MVs) are central to AV1’s hybrid coding architecture. For each coding block, the encoder predicts the motion by estimating a displacement vector pointing from the current block to the best matching region in a reference frame. The prediction mechanism minimizes a cost function of the form:

$$C(\mathbf{MV}) = D(\mathbf{MV}) + \lambda \cdot R(\mathbf{MV})$$

where $D(\mathbf{MV})$ measures blockwise distortion (typically SAD or SSE between the displaced reference and the current block), $R(\mathbf{MV})$ is the number of bits required to encode the MV (including possible residuals), and $\lambda$ is the Lagrange parameter for RD trade-off (Vibhoothi et al., 8 Apr 2024).

Table: Block Partitioning and Motion Vector Allocation

Block Type	Motion Vector Granularity	Typical Use Case
Inter-blocks	Block/subblock-level	Standard motion estimation
Texture mode	Whole-region/global	Homogeneous texture, reduced coding
Intra-blocks	None	Spatial-only, no inter prediction

The encoding syntax allows MVs to be expressed at various block sizes through a quadtree partitioning scheme (Faundez-Zanuy et al., 2022); AV1 also supports hierarchical sub-pixel refinement (down to ⅛-pixel in libaom low cpu-used modes), which ensures sub-pixel localization for improved fidelity (Zouein et al., 20 Oct 2025).

2. Estimation and Optimization Algorithms

AV1 encoders deploy both classic and advanced search algorithms for MV estimation:

• Full/fast block matching: Applies a block search (exhaustive or pruned) to find the best vector per block (e.g., libaom’s exhaustive hierarchical refinement at cpu-used ≤6 vs. aggressive pruning at higher presets) (Zouein et al., 20 Oct 2025).
• Global motion model: For texture or camera-motion regions, AV1 may use a single affine/projective model estimated via RANSAC and FAST features fitted over regions identified as globally consistent (Chen et al., 2018).
• Single-pass look-ahead: Modern SVT-AV1 encoders precompute motion vector fields over a lookahead window (LAD), which are then reused during actual coding to accelerate decision-making (no redundant search), aiding both rate control and computational efficiency (Vibhoothi et al., 8 Apr 2024).

Neural-network driven approaches, such as classification/regression FCNNs, further enhance the predicted motion vector (PMV) accuracy—reducing error versus conventional median predictors and delivering significant bitrate savings (e.g., ≈34% on high-motion data) (Birman et al., 2020). Self-supervised convolutional architectures (CBT-Net) trained with MS-SSIM loss optimize perceptual quality directly in blockwise MV prediction—yielding both BD-rate reduction (–1.7% on MS-SSIM) and computational speed-up (Paul et al., 2021).

3. Specialized Modes: Texture Mode and Hierarchical Partitioning

In "texture mode," dense per-block motion vectors are replaced by a region-specific global affine parameterization. Regions deemed "perceptually insignificant" (via CNN-based segmentation) are synthesized from reference frames using a single transformation:

$$\begin{pmatrix} x' \ y' \end{pmatrix} = \begin{pmatrix} a & b \ d & e \end{pmatrix} \begin{pmatrix} x \ y \end{pmatrix} + \begin{pmatrix} c \ f \end{pmatrix}$$

(Chen et al., 2018, Chen et al., 2019). Only the affine parameters (texture motion parameters) are transmitted, eliminating residual and blockwise vector signaling for these areas, with data rate reductions observed especially at low quantization parameters.

In variable block-size estimation, a quadtree or bottom-up merging approach clusters regions of near-homogeneous motion, so that large, uniform-motion blocks are assigned a single MV, further reducing redundancy and overhead (Faundez-Zanuy et al., 2022).

4. Impact on Rate–Distortion, Bitrate Estimation, and Complexity Control

The efficiency of MV estimation and reuse directly affects rate–distortion performance. Metrics such as BD-rate and bitrate savings are used to quantify improvements. Notably:

• Rich lookahead-driven MV reuse enables competitive single-pass encoding, as precomputed vectors (and motion estimation errors) inform RDO decisions without incurring further compute (Vibhoothi et al., 8 Apr 2024).
• Bitrate estimation before full encoding can be performed by aggregating block-level motion search errors, with simple models:

$$\mathrm{MSE}_{\mathrm{block}} = \min\{\mathrm{MSE}_I, \mathrm{MSE}_\mathrm{MV}\} \ k = \lceil \log_2 \mathrm{MSE}_{\mathrm{block}} \rceil$$

yielding strong predictive power (Pearson correlation >0.96 when combined with random forest regressors) for final encoded bitrate (Eichermüller et al., 8 Jul 2024).

Efficiency is further improved by fast block structure algorithms leveraging the invariance of MV-driven partitioning across resolutions, achieving encoding time reductions of 30–36% at minimal BD-rate penalty (Guo et al., 2018).

5. Motion Vectors as a Computer Vision Resource

AV1 motion vectors serve as a cost-effective, high-fidelity surrogate for dense optical flow when extracted from the compressed domain:

• Fidelity: Median EPE values for AV1 MVs (lळibaom-6: 0.34 pixels global, 0.36 in "sky" zones) are close to those of HEVC and suitable for many computer vision tasks (Zouein et al., 20 Oct 2025).
• Pipeline Acceleration: As "warm-start" priors for state-of-the-art flow networks (e.g. RAFT), AV1 MVs accelerate convergence (5 iterations yield EPE ≈1.68 vs. 20 iterations needed for EPE ≈2.07 without warm-start), reducing computation and energy (Zouein et al., 20 Oct 2025, Zhou et al., 2023).
• Dense Feature Matching and SfM: Repurposing MVs for frame-to-frame correspondences enables rapid extraction of dense, sub-pixel matches for structure-from-motion (SfM) pipelines. The process involves mapping block centerpoints plus vector shifts, then propagating across frames and filtering via cosine consistency to retain physically plausible tracks (Zouein et al., 20 Oct 2025).

Table: AV1 Motion Vectors for Dense Correspondences

Step	Mathematical Operation
MV extraction (per block)	$s_n(b) = (x_b, y_b)$, $t_{n,m}(b) = s_n(b) + v_{n,m}$
Cosine consistency filter	$$\frac{\langle v_{n,m}, v_{m,\ell} \rangle}{\|v_{n,m}\| \cdot \|v_{m,\ell}\|} \geq 1 - \epsilon$$

These compressed-domain strategies yield denser matches and competitive geometric accuracy versus SIFT at a fraction of the CPU requirement.

6. Integration into Advanced Analytics and Multimodal Models

Leveraging AV1 MVs for downstream analytics (e.g., action recognition, video MLLMs, bitrate estimation):

• Action Recognition: Motion vector refinement (confidence-weighted and filtered) produces temporal features for CNNs, yielding close-to-optical-flow accuracy with negligible overhead in real-time surveillance, streaming, or mobile settings (Cao et al., 2019).
• Scalable Multimodal Models: Systems like EMA (Zhao et al., 17 Mar 2025) integrate AV1 MVs and I-frame features in slow-fast architectures, with motion encoders operating on "patchified" motion fields, yielding compact and informative representations for tasks including video understanding and question answering.
• Camera Activity Classification: Histograms of MV magnitude and orientation, combined with K–NN classifiers, enable automated camera motion classification with ~79% accuracy (Esakki, 2021).

7. Limitations, Trade-offs, and Future Directions

Practical deployment of AV1 MVs involves balancing the following considerations:

• Encoding complexity vs. MV fidelity: Lower cpu-used settings yield more precise MVs (and thus better downstream accuracy) but at higher computational cost (Zouein et al., 20 Oct 2025).
• Block artifacts in sparse flow fields: Without dense upsampling/refinement, blockwise MVs can cause discontinuities; deep models (e.g. RAFT, MVFlow) mitigate these via iterative refinement or attention-based fusion (Zhou et al., 2023).
• Region-specific limitations: Texture-mode’s global motion parameters introduce risk of warping artifacts in high-motion or mixed-content blocks, requiring careful segmentation, temporal/spatial correction, and sometimes compound prediction for artifact suppression (Chen et al., 2018, Chen et al., 2019).
• Adaptability to content: For scenes with complex or non-affine motion, fixed block sizes or affine models may underperform; adaptive partitioning and learned motion models are active research areas (Paul et al., 2021, Faundez-Zanuy et al., 2022).

The possibility of integrating learnable motion representations (binary motion codes, neural PMV predictors) alongside or in place of block MVs suggests hybrid solutions for future AV1 evolutions, combining rate efficiency, fidelity, and parallel decode (Nortje et al., 2019, Birman et al., 2020).

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In summary, AV1 motion vectors constitute an efficient, versatile, and increasingly high-fidelity signal for both compression and vision applications, with evolving methodologies encompassing classical search, data-driven predictors, and task-adaptive refinements. Their centrality in the codec’s architecture is paralleled by their utility in accelerated analytics, compressed-domain vision, and emerging neural video modeling.