Efficient Video Sampling (EVS)

• Efficient Video Sampling (EVS) is an approach that reduces computational, memory, and storage burdens in video analysis by exploiting temporal redundancy.
• It employs a patch-based strategy with change detection and dynamic thresholding to prune static tokens, enhancing efficiency without retraining models.
• Experimental evaluations show that EVS can achieve up to 4× reduction in latency while preserving semantic fidelity, scaling VLM inputs for long-duration clips.

Efficient Video Sampling (EVS) refers to algorithmic strategies and system designs for reducing the computational, memory, and storage burdens required to process, summarize, or analyze video data, while preserving the essential content and semantics of the source video. Efficient sampling is increasingly critical in the context of vision-LLMs (VLMs) and LLMs that are tasked with processing long video sequences. Recent EVS methods specifically focus on exploiting temporal redundancy—particularly, the fact that many spatial regions (tokens/patches) remain unchanged across consecutive frames—allowing aggressive reduction in the input size with minimal loss in task performance. The following sections provide a detailed account anchored in current research, with an emphasis on the 2025 EVS framework for VLMs (Bagrov et al., 16 Oct 2025).

1. Motivation and Problem Statement

The expansion of VLMs from static-image to video understanding introduces a scalability bottleneck owing to the quadratic computational cost of transformer-based architectures in both space and time. Dense frame sampling in long videos routinely exceeds the token and context window budgets of contemporary LLMs, leading to increased inference latency (e.g., time-to-first-token, TTFT), memory overhead (notably in key-value caches), and limited ability to handle longer or multi-stream video inputs.

Temporal redundancy is pervasive in natural videos: most spatial patches across consecutive frames are static. Naive processing pipelines that tokenize all patches from every frame lead to significant waste, since many tokens carry redundant information. Efficient Video Sampling seeks to systematically prune such redundancy, thereby enhancing model throughput without compromising semantic or task fidelity.

2. Methodology: Patch-Based Redundancy Pruning

The EVS algorithm operates as a plug-and-play module that identifies and removes temporally static patches (tokens) across video frames prior to inputting them into a vision-LLM. The canonical workflow involves the following steps:

• Frame Partitioning: Videos are spatially divided into non-overlapping square patches, each patch serving as a visual token.
• Change Detection: For each patch position $p$ and frame index $t > 0$, compute a dissimilarity metric between the patch and its temporal predecessor. In RGB space, this is typically the $L_1$ norm:

$D_{p, t} = \|p_t - p_{t-1}\|_1.$

In embedding space, a cosine dissimilarity over the feature dimension is used.

• Dynamic Thresholding: For every frame or for the sequence as a whole, determine a threshold $d$ based on a target pruning quantile $q$ (percentile). Only patches where $D_{p, t} \geq d$ are retained.
• Token Selection and Positional Identity: The resultant binary mask $M$ indicates kept tokens. Critically, two strategies for updating position IDs are considered:
  • Sequential Reindexing: Renumbering position IDs consecutively post-pruning.
  • Position-Preserving: Retaining the original spatial-temporal position IDs, which is superior for transformer-based LLMs that use fixed positional embeddings.
• Uptraining for Robustness: To further improve tolerance to various pruning levels, an additional short fine-tuning phase is recommended where pruning rates $q \sim \mathrm{Beta}(\alpha\approx0.75)$ are stochastically sampled.

This methodology is parameter-free during inference, requires no model retraining if used in plug-and-play mode, and is compatible with both RGB and embedding space computations. Running in RGB space avoids the need for multiple passes through the vision encoder, which is particularly advantageous in low-latency or streaming scenarios.

3. Mathematical Foundations and Computational Impact

The pruning step introduces several key computational improvements and is underpinned by explicit mathematical formalizations:

• Token and KV-Cache Savings: Let $S$ be the effective number of post-pruning tokens per sequence; the memory required for storing the attention key-value (KV) cache is approximately

$$M_{\mathrm{kv}} = \frac{1}{2^{20}} \cdot S \cdot (B+Q) \cdot D_{\mathrm{kv}} \cdot s_{\mathrm{kv}},$$

where $B$ and $Q$ are batch and query dimensions, $D_{\mathrm{kv}}$ is the per-token dimension, and $s_{\mathrm{kv}}$ is storage size (bytes).

• Latency Reduction: Experiments show that aggressive pruning—e.g., with $q = 0.75$ (retaining only the top 25% most dynamic tokens)—yields up to 4× reduction in time-to-first-token for the LLM, as well as linear decreases in overall memory usage (Bagrov et al., 16 Oct 2025).
• Semantic Fidelity: Despite significant reduction in input size, EVS preserves both semantic and positional information, avoiding degradation in benchmark performance on tasks including video question answering, temporal reasoning, and comprehensive video-language understanding (VideoMME, nv-Metropolis, TempCompass, MVBench).

4. Practical Challenges and Solutions

Several technical challenges are highlighted and addressed within the EVS framework:

• Preservation of Positional Structure: Maintaining spatial and temporal position IDs is crucial for transformers reliant on positional encodings. Position-preserving token selection empirically outperforms naive reindexing, particularly after uptraining with stochastic pruning rates.
• Adaptive Pruning Rate: Excessively aggressive pruning could result in information loss. Both plug-and-play and uptrained models benefit from dynamically adjusting the threshold per sequence based on the content's level of motion or change.
• Real-time and Streaming Compatibility: Performing pruning directly in RGB space enables real-time operation without demands on decoder bandwidth or vision encoder resources.

5. Experimental Evaluation and Validation

A suite of ablation and end-to-end experiments on multiple retrieval and reasoning benchmarks demonstrates:

• Efficiency-Accuracy Tradeoff: At pruning rates removing 75% of tokens, LLM TTFT and total pipeline latency are substantially reduced without measurable drops in task accuracy.
• Memory Scaling: The capacity to scale VLM inputs to longer-duration clips is enabled due to the sublinear increase in token count with video length.
• Comparative Performance: EVS consistently outperforms alternative token-reduction strategies, including naive frame-skipping, random token dropping, and patch merging.

The inclusion of uptraining with stochastic pruning rates provides further robustness, ensuring that models generalize well to varied degrees of temporal redundancy at inference time.

6. Extended Applications and Future Directions

EVS substantially unlocks the scalability of VLMs for long-video, multi-stream, and real-time applications, notably without architectural changes or retraining requirements. Promising avenues for future research outlined include:

• Integration into Vision Encoders: Joint prediction of pruning masks earlier in the encoding pipeline to allow for early exit or reduced forward computation.
• Query- or Task-Aware Pruning: Dynamic, context-sensitive masking that leverages language or instruction signals to focus computation on most relevant video content.
• Streaming Adaptation: Extension to online/streaming settings, where both keyframe detection and patch redundancy can be leveraged for adaptive frame selection.
• Combination with Long-Context LLMs: The decoupling of effective sequence length from actual video length via sublinear token growth paves the way for enhanced temporal reasoning across much longer video contexts.

7. Broader Implications

The EVS approach demonstrates that exploiting temporal redundancy at the patch/token level is both a practical and theoretically grounded strategy for efficient video processing in large-scale multimodal models. The concept is generalizable across architectures, tasks, and domains, and can be instantiated either as a post-processing module or via upstream integration. Its parameter simplicity, plug-and-play compatibility, and empirical efficacy position it as a reference method within the emerging corpus of efficient video understanding research.