Model Overview

Description:

DLPP (Deep-Learning Post-Processing) is a family of lightweight deep-learning networks trained for video post-processing. The model's introduction can be found at https://blogs.nvidia.com/blog/rtx-video-super-resolution/. To the public, it is also known as RTX VSR.

Post-processing techniques are used to improve the video quality for the end user. It is usually out of the video encoder and decoder loop so that the engineers can develop and improve it rapidly. Post-processing includes but is not limited to removing encoding artifacts, increasing the resolution, and increasing the frame rate. It has been proven that deep learning models can provide better video quality compared to traditional algorithms.

License/Terms of Use:

DLPP model license in NVIDIA GPU driver: https://www.nvidia.com/en-us/drivers/geforce-license/.
\nDLPP model license in RTX video SDK: https://developer.nvidia.com/downloads/rtx/sdk/rtx_video_sdk_v1.1.0.zip.

Deployment Geography:

Global.

Use Case:

Individual users who own an NVIDIA GPU can use it to improve their video quality.

Release Date:

Build.Nvidia.com 01/30/2023 via https://www.nvidia.com/en-us/drivers/
\nBuild.Nvidia.com 06/04/2024 via https://developer.nvidia.com/rtx-video-sdk/

References:

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)

Network Architecture: U-Net-based CNN architecture

Computational Load:

Cumulative Compute:

< 1*10^23

Estimated Energy and Emissions for Model Training:

\n
Total kWh = 150
\n
\n
Total Emissions (tCO2e) = 0.62
\n

Input(s):

Input Type(s):

Image

Input Format(s):

Image: Red, Green, Blue (RGB)

Input Parameters:

Image: Two-Dimensional (2D)

Other Properties Related to Input:

The input consists of a single RGB image. The model is designed to work primarily with 8-bit and 10-bit input formats. As a pre-processing step, the image must be normalized to the range [0,1]. The supported input resolution is 640x360 to 3840x2160. The input does not include an alpha channel (no transparency) and is represented in [R, G, B] format.

Output(s):

Output Type(s):

Image

Output Format(s):

Image: Red, Green, Blue (RGB)

Output Parameters:

Image: Two-Dimensional (2D)

Other Properties Related to Output:

The output consists of a single RGB image, ranging from 0 to 1. The output does not include an alpha channel (no transparency) and is represented in [R, G, B] format.

Software Integration:

Runtime Engine(s):

CASK SDK.

Supported Hardware Microarchitecture Compatibility:

NVIDIA Turing
NVIDIA Ampere
NVIDIA Lovelace
NVIDIA Blackwell

[Preferred/Supported] Operating System(s):

Windows
Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s):

Version 1.0: First official release, which does super-resolution for videos played in Chrome and Edge browsers.
Version 1.5: Improve the training data and updated the training flow to improve output image quality.
Version 2.0: Added support for quantized CUDA kernels and 10-bit HDR input.

Training, Testing, and Evaluation Datasets:

Training Dataset:

We used internally captured camera footage and gaming recordings to train the model. The captured footage consists of diverse visual scenarios and various popular games.

Data Modality:

Image

Video Training Data Size:

Less than 10,000 Hours

Data Collection Method by dataset:

Human

Labeling Method by dataset:

Not Applicable

Properties:

We captured high-quality camera footage in the wild. The capture resolution is either 4K or over 4K, and does not contain any faces of passersby or anyone close-up. We have also collected high-quality, 4K recordings of popular games in the market. No personal data or confidential data is involved.

Dataset License(s):

Testing Dataset:

Data Collection Method by dataset:

Human

Labeling Method by dataset:

Not Applicable

Properties:

Same as the training dataset.

Dataset License(s):

Evaluation Dataset:

Evaluation Result:

The DLPP model is evaluated mainly based on subjective quality assessment, in which engineers are invited to rate the output video quality. Their ratings are collected and averaged to compute the Mean Opinion Score (MOS). A Non-reference video quality evaluation metric NIQE is also employed. The evaluation results on the evaluation dataset are presented below.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	NIQE score on evaluation dataset
Bicubic	6.4278
DLPP ultra	5.4197
DLPP high	5.7380
DLPP medium	5.5502
DLPP low	5.3699

Data Collection Method by dataset

Human

Labeling Method by dataset:

Not Applicable

Properties:

A few dozen popular YouTube videos are used for evaluation.

Dataset License(s):

Inference:

Engine: CASK SDK
\nTest Hardware:

RTX 20 series
RTX 30 series
RTX 40 series
RTX 50 series

Realtime Inference Latency:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	Runtime on 1920x1080 input image on RTX4090(ms)
DLPP ultra	2.59
DLPP high	1.90
DLPP medium	0.78
DLPP low	0.61

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n**Input Type(s):** \n- Image
\n\n**Input Format(s):** \n- Image: Red, Green, Blue (RGB)
\n\n**Input Parameters:** \n- Image: Two-Dimensional (2D)
\n\n**Other Properties Related to Input:** \n- The input consists of a single RGB image. The model is designed to work primarily with 8-bit and 10-bit input formats. As a pre-processing step, the image must be normalized to the range \\[0,1\\]. The supported input resolution is 640x360 to 3840x2160. The input does not include an alpha channel (no transparency) and is represented in \\[R, G, B\\] format.
\n\n## Output(s):\n\n**Output Type(s):** \n- Image
\n\n**Output Format(s):** \n- Image: Red, Green, Blue (RGB)
\n\n**Output Parameters:** \n- Image: Two-Dimensional (2D)
\n\n**Other Properties Related to Output:** \n- The output consists of a single RGB image, ranging from 0 to 1\\. The output does not include an alpha channel (no transparency) and is represented in \\[R, G, B\\] format.
\n\n## Software Integration:\n\n**Runtime Engine(s):** \n- CASK SDK.\n\n**Supported Hardware Microarchitecture Compatibility:** \n* NVIDIA Turing \n* NVIDIA Ampere \n* NVIDIA Lovelace \n* NVIDIA Blackwell\n\n\n**\\[Preferred/Supported\\] Operating System(s):** \n\n* Windows \n* Linux\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
\n\nThis AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
\n\n## Model Version(s):\n\n* Version 1.0: First official release, which does super-resolution for videos played in Chrome and Edge browsers. \n* Version 1.5: Improve the training data and updated the training flow to improve output image quality. \n* Version 2.0: Added support for quantized CUDA kernels and 10-bit HDR input.\n\n# Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\nWe used internally captured camera footage and gaming recordings to train the model. The captured footage consists of diverse visual scenarios and various popular games.\n\n**Data Modality:**\n- Image\n\n**Video Training Data Size:**\n- Less than 10,000 Hours\n\n**Data Collection Method by dataset:**\n\n- Human\n\n**Labeling Method by dataset:**\n- Not Applicable\n\n**Properties:** \n\nWe captured high-quality camera footage in the wild. The capture resolution is either 4K or over 4K, and does not contain any faces of passersby or anyone close-up. We have also collected high-quality, 4K recordings of popular games in the market. No personal data or confidential data is involved.\n\n**Dataset License(s):**\n- N/A\n\n## Testing Dataset:\n\n**Data Collection Method by dataset:**\n- Human\n\n**Labeling Method by dataset:**\n- Not Applicable\n\n**Properties:** \n\n- Same as the training dataset.\n\n**Dataset License(s):**\n- N/A\n\n## Evaluation Dataset:\n\n**Evaluation Result:** \n\nThe DLPP model is evaluated mainly based on subjective quality assessment, in which engineers are invited to rate the output video quality. Their ratings are collected and averaged to compute the Mean Opinion Score (MOS). A Non-reference video quality evaluation metric NIQE is also employed. The evaluation results on the evaluation dataset are presented below.\n\n| Model | NIQE score on evaluation dataset |\n| :---: | :---: |\n| Bicubic | 6.4278 |\n| DLPP ultra | 5.4197 |\n| DLPP high | 5.7380 |\n| DLPP medium | 5.5502 |\n| DLPP low | 5.3699 |\n\n**Data Collection Method by dataset** \n\n- Human\n\n**Labeling Method by dataset:**\n- Not Applicable\n\n**Properties:** \n\n- A few dozen popular YouTube videos are used for evaluation. \n\n\n**Dataset License(s):**\n- N/A\n\n\n## Inference:\n\n**Engine:** CASK SDK \n**Test Hardware:** \n- RTX 20 series\n- RTX 30 series\n- RTX 40 series\n- RTX 50 series\n\n**Realtime Inference Latency:** \n\n| Model | Runtime on 1920x1080 input image on RTX4090(ms) |\n| :---: | :---: |\n| DLPP ultra | 2.59 |\n| DLPP high | 1.90 |\n| DLPP medium | 0.78 |\n| DLPP low | 0.61 |\n\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","DLPP",["D",1770610405681],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/multimedia/models/dlpp","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Not Applicable |\nMeasures taken to mitigate against unwanted bias: | Not Applicable |\nBias Metric (If Measured): | None |","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Super Resolution and Image Restoration. |\nModel Type: | CNN. |\nIntended Users: | End-users of our GPUs. |\nOutput: | Red, Green, Blue (RGB) images. |\nDescribe how the model works: | The model takes RGB images and improves their visual quality by removing compression artifacts and increasing resolution. |\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\nTechnical Limitations & Mitigation: | Real-world videos often suffer from multiple types of degradation, such as blur, noise, and compression artifacts. Our model is trained on limited types of degradation, it may struggle to address all these issues simultaneously. |\nVerified to have met prescribed NVIDIA quality standards: | Yes |\nPerformance Metrics: | Mean Opinion Score. |\nPotential Known Risks: | This model may create unwanted patterns in the output video when the input video is heavily distorted. |\nLicensing: | http://nvidia.com/en-us/drivers/geforce-license/ |","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No |\nPersonal data used to create this model? | No |\nWas consent obtained for any personal data used? | Not Applicable |\nHow often is dataset reviewed? | Dataset is initially reviewed upon addition, and subsequent reviews are conducted as needed or upon request for changes. |\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable |\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable |\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\nWas data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No. |\nIs there provenance for all datasets used in training? | Yes |\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes |\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\nApplicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Video post-processing. |\nDescribe the life critical impact (if present). | Not Applicable \nUse Case Restrictions: | This model is not designed for safety critical applications. |\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[],"displayLabels",[263,264,265,266,267,268,269],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_227":295,"_271":296,"_273":297,"_275":276},{"_227":292,"_271":293,"_273":294,"_275":276},{"_227":289,"_271":290,"_273":291,"_275":276},{"_227":285,"_271":286,"_273":287,"_275":276},{"_227":283,"_271":283,"_273":284,"_275":276},{"_227":279,"_271":280,"_273":281,"_275":276},{"_227":270,"_271":272,"_273":274,"_275":276},"Vision AI","value","soln_vision_ai","filter",{"_277":278,"_271":272},"category","label","field","technology","Video enhancement","uscs_video_enhancement",{"_277":282,"_271":280},"application","NSPECT-MEXL-YINY",{"_277":276,"_271":283},"CUDA Toolkit","pltfm_cuda_toolkit",{"_277":288,"_271":286},"platform","CUDA","pltfm_cuda",{"_277":288,"_271":290},"Computer Vision","soln_computer_vision",{"_277":278,"_271":293},"AI","soln_ai",{"_277":278,"_271":296},{"_113":1431,"_115":1432},"ngcApps",[1384],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1366":1367,"_1368":1369,"_1370":1371,"_1372":1373,"_1374":309,"_145":335,"_1375":71,"_1376":56,"_1377":92,"_1378":92,"_1379":1380,"_1381":92,"_1382":1383},"amplitude",{"_1363":1364,"_970":-7,"_1365":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1357":1358,"_1359":1360,"_1361":1362},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1792821243509,"1.28.1","defaultSearchSuggestions",{"_1240":177,"_1241":1242,"_1243":1244,"_1245":1246},"announcements",[],"consentState",{"_373":1230,"_1231":31,"_1232":31,"_1233":92,"_1234":1235},"consentConfig",{"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374},"showContactAdmin",1792821243510,[],[],[349,350,351,352,353,354],{"_45":59,"_47":70,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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Name	Size	Updated	Actions
libnvsdk.zip	26.52 MB	February 9, 2026 UTC	curl wget Download
RTX_Video_SDK_v1.1.0.zip	60.55 MB	May 29, 2025 UTC	curl wget Download