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","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. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559],[398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414],{"_415":521,"_232":521,"_196":197,"_388":522},{"_415":516,"_232":516,"_196":197,"_388":517},{"_415":512,"_232":512,"_196":197,"_388":513},{"_415":507,"_232":507,"_196":197,"_388":508},{"_415":502,"_232":502,"_196":197,"_388":503},{"_415":490,"_232":490,"_196":197,"_388":491},{"_415":478,"_232":478,"_196":197,"_388":479},{"_415":473,"_232":473,"_196":197,"_388":474},{"_415":462,"_232":462,"_196":197,"_388":463},{"_415":457,"_232":457,"_196":197,"_388":458},{"_415":452,"_232":452,"_196":197,"_388":453},{"_415":447,"_232":447,"_196":197,"_388":448},{"_415":442,"_232":442,"_196":197,"_388":443},{"_415":437,"_232":437,"_196":197,"_388":438},{"_415":432,"_232":432,"_196":197,"_388":433},{"_415":425,"_232":425,"_196":197,"_388":426},{"_415":416,"_232":416,"_196":197,"_388":417},"hostName","amazon-adsystem.com",[418,419],{"_82":424,"_421":416,"_422":35,"_196":423},{"_82":420,"_421":416,"_422":35,"_196":423},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[427,428],{"_82":431,"_421":425,"_422":35,"_196":430},{"_82":429,"_421":425,"_422":35,"_196":430},"IDE","This domain is owned by Doubleclick (Google). 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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[464,465,466,467],{"_82":472,"_421":462,"_422":35,"_196":469},{"_82":471,"_421":462,"_422":35,"_196":469},{"_82":470,"_421":462,"_422":35,"_196":469},{"_82":468,"_421":462,"_422":35,"_196":469},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[475],{"_82":476,"_421":473,"_422":35,"_196":477},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[480,481,482,483],{"_82":489,"_421":478,"_422":35,"_196":485},{"_82":487,"_421":478,"_422":35,"_196":488},{"_82":486,"_421":478,"_422":35,"_196":485},{"_82":484,"_421":478,"_422":35,"_196":485},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[492,493,494,495],{"_82":501,"_421":490,"_422":35,"_196":499},{"_82":500,"_421":490,"_422":35,"_196":499},{"_82":498,"_421":490,"_422":35,"_196":499},{"_82":496,"_421":490,"_422":35,"_196":497},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[504],{"_82":505,"_421":502,"_422":35,"_196":506},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[509],{"_82":510,"_421":507,"_422":35,"_196":511},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[514],{"_82":515,"_421":512,"_422":35,"_196":456},"R","6sc.co",[518],{"_82":519,"_421":516,"_422":35,"_196":520},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667],[612,613,614,615,616],{"_415":507,"_232":507,"_196":197,"_388":638},{"_415":473,"_232":473,"_196":197,"_388":633},{"_415":626,"_232":626,"_196":197,"_388":627},{"_415":622,"_232":622,"_196":197,"_388":623},{"_415":617,"_232":617,"_196":197,"_388":618},"nvcrm.my.site.com",[619],{"_82":620,"_421":617,"_422":35,"_196":621},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[624],{"_82":625,"_421":622,"_422":35,"_196":456},"edge","github.com",[628,629],{"_82":632,"_421":626,"_422":35,"_196":631},{"_82":630,"_421":626,"_422":35,"_196":631},"logged_in","This domain is owned by Github. 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