The Quality Score
Every Frame Deserves.
No Source Needed.

 

Traditional video quality tools require a reference clip to compare against. AuroraVQA doesn’t. It scores any video — live stream, UGC upload, or AI-generated content — exactly the way a human eye would.

 

Industry-leading accuracy. PLCC 0.9328 and SRCC 0.9368 on Visionular’s proprietary dataset.

 

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See Live Scores
Quality intelligence
that works in the real world

Blind Assessment

Unlike PSNR, SSIM, or VMAF, AuroraVQA needs no original source file. Score quality directly at ingest, on live streams, or across millions of UGC uploads — without ever having the source clip.

Aligned to Human Perception

Trained on years of human viewing data, AuroraVQA scores what viewers actually notice — sharpness, blocking, color fidelity, aesthetics — not just mathematical pixel differences.

Production-Ready Speed

Fast enough for live monitoring. Lightweight enough to run at scale across VOD libraries or real-time upload pipelines — without heavy infrastructure overhead.

See AuroraVQA score real videos

Select a sample to see quality scores across all dimensions — the same analysis your production pipeline would receive.

20 metrics across 4 quality dimensions

Every video is evaluated across four independent domains — each capturing a different aspect of the viewing experience.

Usability

Is the video watchable at all? Catches critical playback failures automatically before content enters any encoding or distribution pipeline.

Visual Glitching | Interlacing Artifacts
Sandwich Effect |  Distortion

Quality Damage

Encoding and transmission artifacts that degrade the viewing experience. Each detected and scored independently across every frame.

Blur | Over Sharpening
Detail Loss | Blocking Artifacts
Noise | Dirty Lens
Underexposure | Overexposure

Aesthetics

Visual appeal beyond technical accuracy. Scored to surface the best content in recommendation and ranking systems.

Colour | Contrast | Aperture

AIGC Detection

Identifies AI-generated content and verifies whether visuals match their source prompts. Enables automated quality gates for AIGC platforms before content goes live.

AI Detection
Prompt-Video Consistency

Quality intelligence at every stage of the pipeline

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Video Encoding & Enhancement

Score quality before and after encoding to optimize parameters, validate enhancement gains, and decide which content benefits from AI upscaling — all without manual review.

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UGC Content Filtering

Automatically grade millions of daily uploads at ingest. Filter out low-quality content, flag encoding issues, and enforce platform quality standards without a human review bottleneck.

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AIGC Quality Control

Filter training datasets before model ingestion. Gate AIGC outputs before publication with automated scoring across aesthetics, artifact detection, and prompt consistency. Ensure outputs meet quality thresholds before publication.

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Content Recommendation

Feed AuroraVQA scores directly into ranking signals. Surface visually high-quality content to improve CTR, watch time, and retention — while suppressing low-quality results algorithmically.

Who’s Using Aurora-VQA

Helping global platforms deliver consistent, high-quality video at scale.

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Video Quality Optimization

With hundreds of millions of daily uploads, Snapchat uses Aurora-VQA to automatically identify source quality, detect encoding artifacts, and trigger adaptive enhancement — reducing manual tuning and improving viewing consistency at scale.

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Video Quality & Enhancement

Alipay relies on Aurora-VQA to power intelligent video quality assessment across its platform — ensuring encoding and delivery meet top-tier perceptual standards for hundreds of millions of users, including high-demand live events.

GET STARTED
Ready to make quality your competitive advantage?
Talk to our team about integrating AuroraVQA into your pipeline. Private deployment or Cloud API — we’ll benchmark your content for free.