Keyframe-Based Temporal Compression Overview

Updated 12 November 2025

KTC is a compression technique for video and sequential data that selects informative keyframes to reduce redundancy and maintain quality.
It employs optimization methods such as IQP, peak detection, and hybrid metrics to balance relevance and diversity in keyframe selection.
KTC integrates reconstruction techniques like motion compensation and diffusion-based synthesis to effectively restore missing temporal information.

Keyframe-Based Temporal Compression (KTC) comprises a family of algorithmic and architectural approaches that maximize temporal compression in video and sequential data by selecting, transmitting, or explicitly modeling a small subset of “key” frames or moments, together with auxiliary mechanisms for reconstructing or summarizing the full sequence. KTC strategies are fundamental in modern video coding, long-context video reasoning, generative video synthesis, 3D mesh compression, latent video modeling, video-based planning, and robotic perception. These systems exploit inter-frame redundancy, semantic change, and task-driven informativeness to achieve orders-of-magnitude data reduction without substantial loss in downstream utility or perceptual quality.

1. Formulations and Methodological Taxonomy

KTC encompasses a broad algorithmic space unified by the selection of keyframes and the compression of temporal data through explicit or implicit modeling of non-keyframes. Canonical formulations include:

Integer Quadratic Programs (IQP) for query-relevant and diverse keyframe selection in the context of MLLMs, e.g.,

$\max_{\mathbf{x}\in\{0,1\}^N} \sum_{i=1}^N r_i\,x_i -\lambda \sum_{1\le i<j\le N} s_{ij}\,x_i\,x_j \quad\text{s.t.}\quad \sum_{i=1}^N x_i = K$

with $r_i$ denoting relevance and $s_{ij}$ diversity (Fang et al., 30 May 2025).

Peak-based selectors (e.g. DPSelect), identifying local maxima of inter-frame feature distance (Wang et al., 29 Dec 2024).
Hybrid change metrics combining photometric, SSIM, semantic, and motion-based differences for robust, scenario-adaptive keyframe detection (Jha et al., 27 Oct 2025, Wan et al., 24 Nov 2024).

KTC systems typically combine:

Keyframe Selection: Criteria-driven (semantic, motion, error, domain) selection or learned latent-variable identification of $N\ll T$ keyframes.
Non-Keyframe Handling: Transmission of motion vectors, deformation models, token-level summaries, or learned inpainting/reconstruction modules.
Auxiliary Narratives/Descriptors: Integration of captions, hierarchical text, or multiscale latent representations to mitigate temporal discontinuity and knowledge loss.

Both traditional video codecs (GOP/I-frame/P-frame structures) and modern neural approaches (VAE-based, diffusion-based, planning, generative) can be mapped to this taxonomy, with varied trade-offs in complexity, compression, and application scope.

2. Key Algorithmic Approaches

2.1. Query-Aware and Diversity-Driven Selection

In prompt-driven MLLM video QA or video LLMs, KTC focuses on relevance and coverage under context constraints. Nar-KFC (Fang et al., 30 May 2025) formulates keyframe selection as a binary IQP balancing query–frame cosine similarity and pairwise redundancy penalties. Efficient $O(NK)$ -time greedy algorithms with low-rank SVD denoising recover near-optimal sets, making the approach scalable to thousand-frame videos.

Other methods employ peak detection in per-frame feature distance curves (e.g., DPSelect's 1D max-pooling over cosine distances (Wang et al., 29 Dec 2024), or per-patch L1 residuals at low-resolution (Zhuo et al., 2022)), or semantically and motion-composite metrics using CLIP and RAFT kernels (M3-CVC (Wan et al., 24 Nov 2024)), dynamically thresholded error aggregations (photometric/SSIM (Jha et al., 27 Oct 2025)), and learned event predictors (KeyIn (Pertsch et al., 2019)).

2.2. Temporal Propagation, Inpainting, and Narrative Weaving

To reconstruct or summarize video content between sparsified keyframes, KTC is implemented through various mechanisms:

Interleaved textual captions fill temporal gaps (Nar-KFC) with off-the-shelf captioners (e.g., Qwen2-VL-2B), producing a sequence such as

$\{f_{y_1}, c_{y_1+\Delta}, \dots, f_{y_2}, c_{y_2+\Delta}, \dots, f_{y_K}\}$

restoring storyline continuity (Fang et al., 30 May 2025).

Motion Compensation (Fast-Vid2Vid): EPZS patchwise block motion and overlapped blending interpolate frames between keyframes with minimal compute overhead (Zhuo et al., 2022).
Diffusion-based conditional synthesis (M3-CVC): Hierarchical text- and codebook-conditional diffusion models reconstruct keyframes and interpolate intermediates guided by LMM-generated narratives (Wan et al., 24 Nov 2024).
Per-vertex deformation models (Ultron): Mesh sequences are compressed by warping keyframe topology to match dependent frames, subject to geometric and texture quality constraints (Zhu, 8 Sep 2024).
Inpainting LSTM modules (KeyIn): Latent-based sequence generation conditioned on learned keyframe placements and offsets achieves temporally adaptive reconstructions (Pertsch et al., 2019).
Feature warping and context mining (Learned Video Compression): Multi-scale warped features populate encoder/decoder modules to maximize spatial-temporal context for P-frame reconstruction (Sheng et al., 2021).

2.3. Cache and Latent Redundancy Pruning

Modern VideoLLMs achieve further memory reduction by knowledge-aware KV cache pruning (PivotKV (Wang et al., 29 Dec 2024)). Attention scores guide the selective retention of tokens, always preserving pivot (keyframe) tokens and aggressively discarding low-importance context, yielding up to $8\times$ longer supported frame context with $20\%$ decode time reductions.

In latent VAE models, channelwise splits into keyframe-inherited and temporal-convolution branches accelerate convergence and improve spatiotemporal trade-offs, as in IV-VAE's KTC-GCConv block (Wu et al., 10 Nov 2024).

3. Architectural Patterns and System Integration

The integration points for KTC span the frontend, mid-pipeline, and latent levels:

Front-end Data Curation: Adaptive keyframes guide data admission in robot perception, scene reconstruction (e.g., Spann3r, CUT3R backends (Jha et al., 27 Oct 2025)), or as plug-ins to long-context MLLMs and VideoLLMs (Fang et al., 30 May 2025, Wang et al., 29 Dec 2024).
Codec and Representational Bottlenecks: Group-of-Pictures (GOP) division with learnable/engineered I-frames and P-frames in classical and neural codecs (Cheng2020Anchor-based models) (Sheng et al., 2021).
Latent-Space and GAN/VAEs: Hybrid splits and group-causal mechanisms for latent space allocation (IV-VAE KTC/GCC) (Wu et al., 10 Nov 2024), or keyframe-inpainter RNN hierarchies (Pertsch et al., 2019).
Multimodal Summarization: Interleaved vision-and-textual sequences for MLLM-based QA (Fang et al., 30 May 2025, Wan et al., 24 Nov 2024), with direct consumption of dense hybrid compressed streams.

The modularity of these mechanisms allows KTC methods to serve as frontends, post-processing routines, or as differentiable bottlenecks in end-to-end training.

4. Applications and Performance Benchmarks

KTC has achieved strong empirical results across diverse video- and sequence-processing tasks:

Application Area	Main Papers	Typical Gains
Long-video MLLM QA	(Fang et al., 30 May 2025, Wang et al., 29 Dec 2024)	+3–6 pts accuracy, 8× tokens
Learned video codecs	(Sheng et al., 2021, Wu et al., 10 Nov 2024)	−14–21% BD-rate, 98.7% decode speedup
Vid2Vid synthesis	(Zhuo et al., 2022)	8–9× MACs reduce, 6× latency reduce
3D mesh sequences	(Zhu, 8 Sep 2024, Jha et al., 27 Oct 2025)	15–60% compression, minimal mesh loss
Video planning/model	(Pertsch et al., 2019)	Largest F1 for event detection, best success rates

Experimental benchmarks universally demonstrate that KTC, when well-configured, can deliver high compression ratios (up to $95\%$ frame reduction), significant reductions in computational or storage requirements, and in many cases improvements in downstream accuracy or perceptual metrics due to reduction of redundant or irrelevant information.

Notably, M3-CVC (Wan et al., 24 Nov 2024) uses KTC with LMM-guided text and semantic-movement-adaptive keyframe selection to achieve rate–distortion performance beyond VVC (VTM-17.0) at ultra-low bitrates, with BD-Rate savings up to $20\%$ .

5. Design Trade-offs, Limitations, and Robustness

Despite their generality, KTC systems require careful hyperparameter selection (e.g., keyframe counts, selection thresholds). Overspecification or underspecification of $N$ can cause under-representation or loss of critical events (Pertsch et al., 2019). Most approaches must contend with blurriness or loss of fine-scale detail when inpainting or reconstructing highly stochastic or rapidly changing segments, though hybrid models using text, flow, and global context can mitigate this. In learned codecs, the choice of prior and channel splits (e.g., half keyframe, half temporal in IV-VAE) significantly impacts convergence and information flow (Wu et al., 10 Nov 2024).

Decoding and inference latency—as well as memory usage—are key considerations in LLM-based systems. Approaches such as PivotKV and SVD-based greedy selection can ameliorate quadratic scaling with minimal accuracy loss (Wang et al., 29 Dec 2024, Fang et al., 30 May 2025). The cost of running per-frame captioners, CLIP/RAFT, or feature encoders must be weighed against overall system throughput in real-time or streaming applications.

6. Domain Extensions: 3D, Multimodal, and Latent Spaces

KTC generalizes beyond 2D video frames to mesh sequences (Zhu, 8 Sep 2024), depth maps (Jha et al., 27 Oct 2025), and latent space codes (Wu et al., 10 Nov 2024). In 3D, per-segment corner-table compression, geometric deformation, and dynamic thresholding achieve state-of-the-art temporal geometry reduction. In multimodal LMM systems, KTC’s interaction with text narrativization and mixed-modal fusion is vital, as continuity and semantic richness heavily influence downstream QA or planning capability (Fang et al., 30 May 2025, Wan et al., 24 Nov 2024).

Hierarchical hybrid models, extracting intra- and inter-frame descriptive codes (e.g., LMM textual dialogues plus VQVAE/diffusion latents (Wan et al., 24 Nov 2024)), represent a future direction, enabling explainable end-to-end encoding and robust semantic control at low bitrate.

7. Summary and Outlook

Keyframe-Based Temporal Compression methods form the algorithmic backbone for efficient video, sequential, and multimodal data summarization, supporting deployments in resource-constrained inference, scalable perception, and ultra-low bitrate compression. Contemporary KTC methods integrate mathematical optimization, information theory, modern deep learning (VAEs, diffusion, LMMs), and domain-specific event detection to approach the theoretical limits of information retention under given task constraints. As scale grows, the ability to combine adaptive, learned, semantically-informed keyframe selection with efficient non-keyframe reconstruction, hybrid representations, and cache-aware inference will continue to define leading performance in video intelligence, compression, and perception systems.