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Research map / Video / Video segmentation and tracking

Learned video compression

Replaces traditional codecs with end-to-end networks using motion compensation, feature-space prediction and transformers.

68 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2018–2026) · an approach in Video segmentation and tracking, keyword cluster “compression · coding · rate”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

16 papers in 2023–24 and 30 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 3.6% to 5.6%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 0162017: 0172018: 4182019: 3192020: 5202021: 6212022: 4222023: 8232024: 8242025: 16252026: 1426

Most cited paper of each year

Earliest papers here

What has been tried

51 papers here report 489 measured changes (a component added, removed or swapped, compared with a baseline): helped 78%, no clear effect 3%, hurt 17%, mixed 2%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Add structure optimizationAll frames, YUV420, intra-period –1 · BD-Rate (%)0.8HurtNeural Video Compression with Feature Modulation (CVPR 2024 · 140 citations)
use implicit temporal modelling instead of explicit motion modulesMCL-JCV, large motion · BD-Rate0.0% -> 2.1%HurtTowards Practical Real-Time Neural Video Compression (CVPR 2025 · 61 citations)
Add quantization step < 1Kodak · BD-rate vs C3 Adaptive2.6%HelpedC3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video (CVPR 2024 · 50 citations)
Add offline Spynet fine-tuning with VTM motion vectorsAll testing datasets (Average) · BD-Rate (%)-3.9HelpedOffline and Online Optical Flow Enhancement for Deep Video Compression (AAAI 2024 · 26 citations)
add temporal probabilistic predictionUVG · PSNRHelpedDeep Hierarchical Video Compression (AAAI 2024 · 25 citations)
Add motion vectors to the coding-prior-free baselineJCT-VC, LDB, QP 37, average of 18 sequences · ∆PSNR (dB)0.69 -> 0.72HelpedCPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement (CVPR 2024 · 21 citations)
add time-dependent feature stream ftBartender · PSNR30.59 -> 31.94HelpedGIFStream: 4D Gaussian-based Immersive Video with Feature Stream (CVPR 2025 · 14 citations)
Add MNLC to the DCVC-DC baseHEVC B/C/D/E, IP 32 · BD-Rate (%) vs base model−4.0 / −9.9 / −9.0 / −9.7HelpedECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression (CVPR 2025 · 13 citations)
Add flow orientationHEVC datasets, intra-period 32 · BD-Rate (%)0.0 -> -1.9HelpedNeural Video Compression with Context Modulation (CVPR 2025 · 13 citations)
Use DivGoP with Pm frames instead of Pr framesMOT17 · mAP28.68% vs 32.67% bitrate savingsHelpedTask-Aware Encoder Control for Deep Video Compression (CVPR 2024 · 13 citations)
add motion estimationN3DV · BD-PSNR-1.86 dBHelped4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video (CVPR 2025 · 12 citations)
Add tri-plane residual dynamic modelingVolumetric video · bitrateHelpedVRVVC: Variable-Rate NeRF-Based Volumetric Video Compression (AAAI 2025 · 6 citations)

What reviewers will expect

Datasets and baselines named in the 16 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • Vimeo-90k · 7 of 16 papers
  • N3DV · 2 of 16 papers

Baselines

  • DCVC-DC · 7 of 16 papers
  • DCVC-FM · 6 of 16 papers
  • DCVC-TCM · 4 of 16 papers
  • K-Planes · 3 of 16 papers
  • ReRF · 3 of 16 papers
  • DCVC-HEM · 3 of 16 papers
  • TeTriRF · 2 of 16 papers
  • NeRFPlayer · 2 of 16 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 46 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 37 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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