Image & Video Super-Resolution Patents: Leaders & White Space 2026
- Filing peaked in 2017 at 5 records and the trend has been flat or declining since, including a 2022 midpoint of zero — a sign the earliest-filed claims still carry unusual weight.
- One family dominates the citation table US20120288015A1 on example-based super-resolution for video compression is cited 76 times, well ahead of anything else in scope.
- G06T carries 85.2% of the 27 records while H04N sits at 40.7% — most of the claim activity is in image-processing method claims, not transmission-side video coding.
What this dataset covers
This landscape draws on 27 published records filed between 2015 and the 2026 data cut-off, indexed against IPC classes covering image data processing (G06T), AI-based computing (G06N) and pictorial communication (H04N). The search string combines super-resolution and upscaling-network terminology with claim/description language around perceptual-versus-fidelity tradeoff, temporal consistency, real-world degradation modelling, on-device inference and artifact suppression — the vocabulary that separates a genuine super-resolution claim from a generic image-enhancement filing.
Publication lags filing by roughly 18 months, so the most recent year in the trend below is understated by construction — treat 2025 and 2026 figures as a floor, not a ceiling.
Filing trend and technology composition
Two views of the same 27-record set: when the filing happened, and what technical territory it sits in.
Filing trend
Activity peaked at 5 records in 2017. The 2022 midpoint reads zero, and the line stays flat-to-declining through to the partial 2026 count — this is a corpus where the foundational filings came early and the field has not seen a comparable second wave since.
IPC composition
G06T (image data processing & generation) appears in 85.2% of the 27 records, confirming that most claims are about the upscaling/reconstruction method itself. H04N (pictorial communication) at 40.7% and G06N (AI-based computing) at 18.5% show where video-transmission and learned-model claims overlap with that core. Because records can carry multiple IPC classes, these shares add to more than 100% by design.
Shares are the percentage of the 27 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Image and Video Super-Resolution with Eureka
This page is one run against one query. Ask Eureka your own question about image and video super-resolution and every answer comes back with the patent numbers behind it.
Try EurekaThe most-cited records in scope
GB2600787A — Method and apparatus for video super resolution
The claim describes an iterative machine-learning process that alternates between estimating degradation (downsample) kernels for a group of sequential, temporally consistent low-resolution frames and upscaling that group using the estimated kernels, with a kernel estimator and frame restorer pairing that determines feature maps for the low-resolution frames before final upscaling.Filed by Samsung Electronics, published 2022-05-11.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20120288015A1 | Data pruning for video compression using example-based super-resolution | 76 |
| 2 | WO2011090798A1 | Data pruning for video compression using example-based super-resolution | 31 |
| 3 | US20220122223A1 | Kernel-aware super resolution | 26 |
| 4 | US9367897B1 | System for video super resolution using semantic components | 13 |
| 5 | US20160171656A1 | System for video super resolution using semantic components | 12 |
| 6 | US9813707B2 | Data pruning for video compression using example-based super-resolution | 7 |
| 7 | US20180122047A1 | Super resolution using fidelity transfer | 6 |
| 8 | US20160253784A1 | System for video super resolution using semantic components | 6 |
| 9 | US10825138B2 | Super resolution using fidelity transfer | 4 |
| 10 | GB2600787A | Method and apparatus for video super resolution | 3 |
Citation counts inside a searched corpus skew toward older filings — read them as a signal of influence on later claim drafting, not as a measure of current commercial relevance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Three read-throughs from the filing trend, citation table and IPC composition — no figure here beyond what the dataset states.
The wave crested early
Five records in 2017 is the high point of this 27-record set, and the 2022 midpoint drops to zero. Recent-year momentum by assignee across the ranked leaders also reads 0 in the latest year for every name checked, including a -100% YoY figure for one. That pattern is consistent with a field where the foundational claims were staked out once and have not needed heavy re-filing since.
One family anchors the prior art
US20120288015A1, on data pruning for video compression using example-based super-resolution, is cited 76 times — more than double the next entry (WO2011090798A1, the same invention family, at 31). Anyone drafting around example-based or compression-linked super-resolution should expect this family to surface in every relevant search.
Method claims outweigh transmission claims
G06T covers 85.2% of the 27 records against H04N's 40.7%, meaning the bulk of the claim space concerns the reconstruction/upscaling method rather than how the video signal is coded or transmitted. G06N appears in 18.5% of records, marking where learned-model claims are drafted explicitly as AI computing rather than as image processing.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to image and video super-resolution, with the prior art for and against each one.
Who is filing, and where the field is quiet
The assignee ranking returned by this dataset covers 11 companies, from a leader with 7 records down to a tenth-place entrant with 1 — a short list, not a top-50 or top-100 cut.
A leader, then a fast taper
The leading assignee holds 7 records against 3 for fifth place and 1 for tenth — a steep drop-off rather than a gradual one. With only 11 companies in the entire ranking, this is a narrow field where a small number of filers set the terms of the art.
Activity has gone quiet across the board
Every ranked assignee checked for recent-year momentum shows 0 filings in the latest year, including a -100% year-on-year figure. That is unusual for an active AI sub-field and points to either a maturing claim set or a lag in what has published so far.
US-centred, with EPO and PCT as secondary routes
The United States receives the largest share of filings at 14, ahead of Europe (EPO) and WIPO (PCT) at 4 each, and India at 3. A filer targeting only the US risks missing the smaller but real EPO/PCT and India cohorts.
| Assignee | Recent year | YoY |
|---|---|---|
| Samsung Electronics Co., Ltd. | 0 | — |
| MAGIC PONY TECH | 0 | — |
| Sharp Corporation | 0 | — |
| Thomson Licensing SA | 0 | — |
| Dolby Laboratories Licensing Corporation | 0 | — |
| Google LLC | 0 | -100% |
| GE Precision Healthcare LLC | 0 | -100% |
| ZHANG DONG QING | 0 | — |
Where to take this
The dataset points to a narrow, early-staked field with a quiet recent trend — here is how to act on that.
Map claims against the cited core
Before drafting in example-based or kernel-estimation super-resolution, check new claim language against the two highest-cited families in this set, since both are likely to appear in any examiner search.
Explore citation mapping in EurekaTest the under-claimed branches
Real-world degradation modelling and on-device inference show thin coverage relative to the G06T core — worth a freedom-to-operate check before assuming the space is open.
Run a white-space search in EurekaWatch for the publication-lag rebound
A flat 2022-2026 trend combined with an 18-month publication lag means the true recent picture is still forming; re-run this landscape in six to twelve months.
Set a monitoring alert in EurekaCommon questions on super-resolution patents
Within this 27-record dataset, the ranked leader holds 7 records, well ahead of the field, with fifth place at 3 and tenth place at 1. The full ranking covers only 11 companies, so it is a short, concentrated list rather than a broad top-50 field. The steep drop from leader to tenth place suggests a small number of filers have shaped most of the claim language in this space.
US20120288015A1, covering data pruning for video compression using example-based super-resolution, is the most-cited record in this set at 76 citations, with its WIPO counterpart WO2011090798A1 close behind at 31. Both describe the same underlying invention family. Given the citation gap over the next entries, this family is worth checking against any new claim drafted around example-based or compression-linked upscaling.
Based on this dataset, filing peaked in 2017 at 5 records and has been flat or declining since, with the 2022 midpoint at zero and every ranked assignee showing zero filings in the latest year. That said, publication lags filing by roughly 18 months, so the most recent years understate true activity. The honest read is a field that had an early filing wave and has not shown a clear second wave in what has published so far.
GB2600787A, filed by Samsung Electronics and published in 2022, claims an iterative machine-learning process for video super-resolution that alternates between estimating degradation (downsample) kernels across a group of temporally consistent low-resolution frames and upscaling that group using those estimated kernels. It further specifies a kernel-estimator and frame-restorer pairing, with the restorer determining feature maps before final upscaling. Anyone building a kernel-aware, temporally-consistent upscaling pipeline should read this claim closely rather than assume kernel estimation alone is open ground.
The IPC composition shows heavy concentration in G06T (85.2% of 27 records) and H04N (40.7%), with thinner presence in branches like real-world degradation modelling for unseen kernels, on-device inference for mobile deployment, and temporal-consistency loss design. These are described in the underlying claim language of some records but are not the dominant IPC classes, suggesting room for narrowly drawn claims that target degradation modelling or on-device constraints specifically rather than the general upscaling method.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.