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",[246,247,248,249,250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_215":269,"_252":270,"_254":271,"_256":257},{"_215":265,"_252":266,"_254":267,"_256":257},{"_215":262,"_252":263,"_254":264,"_256":257},{"_215":260,"_252":260,"_254":261,"_256":257},{"_215":251,"_252":253,"_254":255,"_256":257},"TensorRT","value","pltfm_tensorrt","filter",{"_258":259,"_252":253},"category","label","field","platform","NSPECT-YCAV-W5KW",{"_258":257,"_252":260},"CUDA","pltfm_cuda",{"_258":259,"_252":263},"Computer Vision","soln_computer_vision",{"_258":268,"_252":266},"technology","AI","soln_ai",{"_258":268,"_252":270},{"_115":1445,"_117":1446},"ngcApps",[1398],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1379":1380,"_1381":1382,"_1383":1384,"_1385":1386,"_1387":283,"_146":307,"_1388":1389,"_1390":57,"_1391":94,"_1392":94,"_1393":1394,"_1395":94,"_1396":1397},"amplitude",{"_1376":1377,"_978":-7,"_1378":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1370":1371,"_1372":1373,"_1374":1375},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1253":176,"_1254":1255,"_1256":1257,"_1258":1259},"announcements",[],"consentState",{"_344":1243,"_1244":94,"_1245":32,"_1246":32,"_1247":1248},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1784000255809,[],[],[321,322,323,324,325],{"_46":61,"_48":72,"_50":110,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":70,"_50":110,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":62,"_50":110,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":110,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":51,"_52":53,"_54":60,"_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 cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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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.","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","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","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.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","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.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.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.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","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 one year lifespan and contains no personal information.","default","

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libnvsalienc.a	262.97 MB	January 7, 2026 UTC	curl wget Download
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