NVSaliENC Overview

Description:

NVSaliENC is an advanced perceptual video encoding module designed to improve visual quality in compressed video streams, specifically targeting critical regions as defined by human visual perception. While each generation of traditional video codecs provides measurable gains in objective quality at fixed bitrates, these improvements often do not correspond to noticeable visual differences for end users. Legacy codecs are limited in their ability to optimize perceptual quality, especially within the 2–5° visual angle around the viewer’s gaze center—a region where the human eye is most sensitive to detail.

NVSaliENC bridges this gap by employing a state-of-the-art deep learning-based saliency prediction model, expertly optimized for real-time inference using NVIDIA TensorRT. It is designed for seamless integration with NVIDIA NVENC, enabling practical and efficient deployment in production workflows. Unlike previous saliency solutions, NVSaliENC achieves real-time performance, making it suitable for latency-sensitive scenarios such as broadcasting, live streaming, and cloud gaming.

By generating high-precision saliency maps, NVSaliENC empowers NVENC to prioritize perceptual video compression for the most visually important regions, thereby enhancing end-user experience while reducing bandwidth usage and computational demand.

License/Terms of Use:

DLPP model license in NVIDIA GPU driver: https://www.nvidia.com/en-us/drivers/geforce-license/.

Deployment Geography:

Global

Use Case:

Individual users who own an NVIDIA GPU and a NVENC can use it to improve the video encoding visual quality.

Release Date:

NGC: 11/21/2025

References

NTECH 2025: NVSaliENC Human Vision-Guided Compression for Psycho-Visual Video Optimization

Model Architecture:

Architecture Type: Mixed Transformer-CNN based Network Architecture
\nNetwork Architecture:

The Mix-UNet network combines the strengths of transformer-based attention and multi-scale convolutional features to accurately capture both local and global visual cues relevant to human attention. We adopt a set of CNN decoders to extract spatial information from multi-scale features, and the residual connections help prevent any loss of information during the downsizing steps of the transformer encoders.

Number of model parameters: 5.2 * 10^7

Input:

Input Type(s): An image and an optional encoding mode
\nInput Format(s): YUV NV12, String
\nInput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Input:

Y channel: B X H X W X 1(Batch Size x Height x Width x Channel)
UV channel: B X H/2 X W/2 X 2(Batch Size x Height/2 x Width/2 x Channel) in interleaved format.
An optional input can be provided to specify the encoding use case: either VBR (variable bitrate) or CQ (constant quality).

Output:

Output Type(s): Image
\nOutput Format: Delta QP map
\nOutput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Output: The output is a delta QP map in block format. Suppose the input resolution is H×W. The block size is 32 for H.265 and 64 for AV1. The dimensions of the delta QP map are (H // block_size, W // block_size). The value range is (-128, 127), where negative QP values allocate more bit resources to the target region.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems None. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), our model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

TensorRT

Supported Hardware Microarchitecture Compatibility:

NVIDIA Turing
NVIDIA Ampere
NVIDIA Jetson
NVIDIA Lovelace

[Preferred/Supported] Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

Version 1.0: first stable release which supports vbr and cq mode. Requirement: TRT 10.14.

Training, Testing, and Evaluation Datasets:

Dataset Overview:

The datasets aim to enhance encoding quality around predicted salient regions, aligning with human vision system.

Training Dataset:

Link: NVSaliENC Datasets: NSPECT-4Q2T-2ZKV

Data Collection Method by dataset: Hybrid: Synthetic, Human.

Labeling Method by dataset:

The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.
The second plane saliency is synthesized by detecting the boxes containing texts.

Properties (Quantity, Dataset Descriptions, Sensor(s)): Resolution: 640x480.

Dataset License(s): NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487

Testing Dataset:

Link: NVSaliENC Datasets: NSPECT-4Q2T-2ZKV

Data Collection Method by dataset: Hybrid: Synthetic, Human.

Labeling Method by dataset:

The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.
The second plane saliency is synthesized by detecting the boxes containing texts.

Properties (Quantity, Dataset Descriptions, Sensor(s)): Resolution: 640x480.

Dataset License(s): NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487

Evaluation Dataset:

Data Collection Method by dataset:

Human

Properties (Quantity, Dataset Descriptions, Sensor(s)): A few hundred videos from customers are used for evaluation.

Inference:

Acceleration Engine: TensorRT

Test Hardware:

[NVIDIA A10]
[NVIDIA T4]

Realtime Inference Latency:
\nThe latency data was obtained when the input resolution was 3840x2160.

\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\">

GPU	Runtime (ms)
A10	2.948
T4	7.700

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 None.

Please report model quality, risk, security vulnerabilities or concerns NVIDIA AI Concerns here.

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While each generation of traditional video codecs provides measurable gains in objective quality at fixed bitrates, these improvements often do not correspond to noticeable visual differences for end users. Legacy codecs are limited in their ability to optimize perceptual quality, especially within the 2–5° visual angle around the viewer’s gaze center—a region where the human eye is most sensitive to detail.\n\nNVSaliENC bridges this gap by employing a state-of-the-art deep learning-based saliency prediction model, expertly optimized for real-time inference using NVIDIA TensorRT. It is designed for seamless integration with NVIDIA NVENC, enabling practical and efficient deployment in production workflows. Unlike previous saliency solutions, NVSaliENC achieves real-time performance, making it suitable for latency-sensitive scenarios such as broadcasting, live streaming, and cloud gaming.\n\nBy generating high-precision saliency maps, NVSaliENC empowers NVENC to prioritize perceptual video compression for the most visually important regions, thereby enhancing end-user experience while reducing bandwidth usage and computational demand. \n\n### License/Terms of Use: \nDLPP model license in NVIDIA GPU driver: https://www.nvidia.com/en-us/drivers/geforce-license/.\n\n### Deployment Geography: \nGlobal\n\n### Use Case: \nIndividual users who own an NVIDIA GPU and a NVENC can use it to improve the video encoding visual quality. \n\n### Release Date: \n**NGC:** 11/21/2025 \n\n### References\n[NTECH 2025](https://nvidia.sharepoint.com/:p:/r/sites/NTECH2/Shared%20Documents/2025/Robotics,%20Vision%20and%20Autonomous%20Vehicles/Presentations/NTECH2025_567_NVSaliENC_Human_Vision-Guided_Compression_for_Psycho-Visual_Video_Optimization.pptx?d=wa272806e966747deb9ee3997bbe98a76&csf=1&web=1&e=qzQFb1): NVSaliENC Human Vision-Guided Compression for Psycho-Visual Video Optimization\n\n## Model Architecture: \n**Architecture Type:** Mixed Transformer-CNN based Network Architecture \n**Network Architecture:** \n\nThe Mix-UNet network combines the strengths of transformer-based attention and multi-scale convolutional features to accurately capture both local and global visual cues relevant to human attention. We adopt a set of CNN decoders to extract spatial information from multi-scale features, and the residual connections help prevent any loss of information during the downsizing steps of the transformer encoders. \n \n**Number of model parameters:** 5.2 * 10^7 \n\n## Input: \n**Input Type(s):** An image and an optional encoding mode \n**Input Format(s):** YUV NV12, String \n**Input Parameters:** Two-Dimensional (2D) \n**Other Properties Related to Input:** \n\n* Y channel: B X H X W X 1(Batch Size x Height x Width x Channel)\n* UV channel: B X H/2 X W/2 X 2(Batch Size x Height/2 x Width/2 x Channel) in interleaved format.\n* An optional input can be provided to specify the encoding use case: either VBR (variable bitrate) or CQ (constant quality). \n\n## Output: \n**Output Type(s):** Image \n**Output Format:** Delta QP map \n**Output Parameters:** Two-Dimensional (2D) \n**Other Properties Related to Output:** The output is a delta QP map in block format. Suppose the input resolution is H×W. The block size is 32 for H.265 and 64 for AV1. The dimensions of the delta QP map are (H // block_size, W // block_size). The value range is (-128, 127), where negative QP values allocate more bit resources to the target region. \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems None. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), our model achieves faster training and inference times compared to CPU-only solutions. \n\n## Software Integration: \n**Runtime Engine(s):** \n* TensorRT \n\n**Supported Hardware Microarchitecture Compatibility:** \n\n* NVIDIA Turing \n* NVIDIA Ampere \n* NVIDIA Jetson \n* NVIDIA Lovelace \n\n**[Preferred/Supported] Operating System(s):** \n\n* Linux \n* Linux 4 Tegra \n\n## Model Version(s): \n\n* Version 1.0: first stable release which supports vbr and cq mode. Requirement: TRT 10.14.\n \n## Training, Testing, and Evaluation Datasets: \n\n### Dataset Overview: \nThe datasets aim to enhance encoding quality around predicted salient regions, aligning with human vision system.\n\n## Training Dataset:\n**Link:** NVSaliENC Datasets: NSPECT-4Q2T-2ZKV \n\n**Data Collection Method by dataset:** Hybrid: Synthetic, Human. \n\n**Labeling Method by dataset:** \n* The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.\n* The second plane saliency is synthesized by detecting the boxes containing texts. \n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Resolution: 640x480.\n\n**Dataset License(s):** NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487 \n\n## Testing Dataset:\n**Link:** NVSaliENC Datasets: NSPECT-4Q2T-2ZKV \n\n**Data Collection Method by dataset:** Hybrid: Synthetic, Human. \n\n**Labeling Method by dataset:** \n* The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.\n* The second plane saliency is synthesized by detecting the boxes containing texts. \n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Resolution: 640x480.\n\n**Dataset License(s):** NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487 \n\n## Evaluation Dataset:\n\n**Data Collection Method by dataset:** \n* Human \n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** A few hundred videos from customers are used for evaluation. \n\n## Inference: \n**Acceleration Engine:** TensorRT \n\n**Test Hardware:** \n* \\[NVIDIA A10\\] \n* \\[NVIDIA T4\\] \n\n**Realtime Inference Latency:** \nThe latency data was obtained when the input resolution was 3840x2160.\n\n| GPU | Runtime (ms) |\n| :---: | :---: |\n| A10 | 2.948 |\n| T4 | 7.700 |\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 None. \n\nPlease report model quality, risk, security vulnerabilities or concerns NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","NVSaliENC",["D",1767746929245],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/multimedia/models/nvsalienc","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":267,"_250":268,"_252":269,"_254":255},{"_213":263,"_250":264,"_252":265,"_254":255},{"_213":260,"_250":261,"_252":262,"_254":255},{"_213":258,"_250":258,"_252":259,"_254":255},{"_213":249,"_250":251,"_252":253,"_254":255},"TensorRT","value","pltfm_tensorrt","filter",{"_256":257,"_250":251},"category","label","field","platform","NSPECT-YCAV-W5KW",{"_256":255,"_250":258},"CUDA","pltfm_cuda",{"_256":257,"_250":261},"Computer Vision","soln_computer_vision",{"_256":266,"_250":264},"technology","AI","soln_ai",{"_256":266,"_250":268},{"_113":1251,"_115":1252},"ngcApps",[1204],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1185":1186,"_1187":1188,"_1189":1190,"_1191":1192,"_1193":281,"_144":307,"_1194":1195,"_1196":57,"_1197":92,"_1198":92,"_1199":1200,"_1201":92,"_1202":1203},"amplitude",{"_1182":1183,"_819":-7,"_1184":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1176":1177,"_1178":1179,"_1180":1181},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789722564865,"1.17.0","defaultSearchSuggestions",{"_1059":174,"_1060":1061,"_1062":1063,"_1064":1065},"announcements",[],"consentState",{"_344":1049,"_1050":32,"_1051":32,"_1052":92,"_1053":1054},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1789722564866,[],[],[321,322,323,324,325],{"_46":60,"_48":71,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":69,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":61,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":51,"_52":53,"_54":55,"_56":57},{"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57},"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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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","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.",4,[576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600],[547,548,549,550,551],{"_385":460,"_213":460,"_173":174,"_358":571},{"_385":434,"_213":434,"_173":174,"_358":566},{"_385":561,"_213":561,"_173":174,"_358":562},{"_385":556,"_213":556,"_173":174,"_358":557},{"_385":552,"_213":552,"_173":174,"_358":553},"nvidia.partners",[554],{"_79":555,"_391":552,"_392":32,"_173":428},"edge","app.hushly.com",[558],{"_79":559,"_391":556,"_392":32,"_173":560},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[563],{"_79":564,"_391":561,"_392":32,"_173":565},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687],[639,640,641,642],{"_385":654,"_213":654,"_173":174,"_358":655},{"_385":649,"_213":649,"_173":174,"_358":650},{"_385":447,"_213":447,"_173":174,"_358":646},{"_385":561,"_213":561,"_173":174,"_358":643},[644],{"_79":645,"_391":561,"_392":92,"_173":565},"_gh_sess",[647],{"_79":648,"_391":447,"_392":92,"_173":454},"YSC","bizible.com",[651],{"_79":652,"_391":649,"_392":32,"_173":653},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[656],{"_79":657,"_391":654,"_392":32,"_173":658},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_79":729,"_391":506,"_392":32,"_173":730},{"_79":714,"_391":728,"_392":92,"_173":715},{"_79":697,"_391":509,"_392":32,"_173":622},{"_79":697,"_391":610,"_392":32,"_173":622},{"_79":726,"_391":506,"_392":32,"_173":727},{"_79":714,"_391":512,"_392":92,"_173":715},{"_79":724,"_391":506,"_392":32,"_173":725},{"_79":714,"_391":509,"_392":92,"_173":715},{"_79":722,"_391":609,"_392":32,"_173":723},{"_79":714,"_391":631,"_392":92,"_173":715},{"_79":694,"_391":509,"_392":32,"_173":696},{"_79":720,"_391":506,"_392":92,"_173":721},{"_79":718,"_391":506,"_392":32,"_173":719},{"_79":716,"_391":506,"_392":92,"_173":717},{"_79":714,"_391":605,"_392":92,"_173":715},{"_79":712,"_391":506,"_392":32,"_173":713},{"_79":710,"_391":506,"_392":32,"_173":711},{"_79":708,"_391":506,"_392":92,"_173":709},{"_79":706,"_391":614,"_392":32,"_173":707},{"_79":694,"_391":605,"_392":32,"_173":696},{"_79":704,"_391":506,"_392":32,"_173":705},{"_79":702,"_391":506,"_392":92,"_173":703},{"_79":700,"_391":506,"_392":32,"_173":701},{"_79":698,"_391":506,"_392":32,"_173":699},{"_79":697,"_391":506,"_392":32,"_173":622},{"_79":694,"_391":695,"_392":32,"_173":696},{"_79":692,"_391":506,"_392":32,"_173":693},{"_79":690,"_391":506,"_392":92,"_173":691},{"_79":688,"_391":506,"_392":32,"_173":689},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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