vmaf-cuda

Description

GPU-accelerated VMAF using CUDA and NVDEC, up to 4.4x throughput and 37x lower latency at 4K versus CPU, now folded into VMAF 3.0/FFmpeg 6.1.

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Related resources

• Encoder-Quantization-Motion (EQM) Video Quality MetricAcademic paper on an encoder-side video quality metric for low-latency use, addressing gaps in VMAF/P.1204.3 via contrast/motion masking an…
• Evaluating Video Quality Metrics for Neural and Traditional Codecs (4K/UHD-1)Academic comparison of VMAF, AVQBits, and FasterVQA no-reference quality metrics on 4K content.
• BsrVideoAnalyzerFFmpeg+SDL based video bitstream analyzer with macroblock/CTU type overlay, I/P/B frame navigation, and bitstream header offset inspection.
• GHVQ: Benchmark and Metric for AI-Generated Human-Activity Video QualityAcademic paper introducing a benchmark dataset and objective quality metric for AI-generated human-activity video, with companion code.
• VQA² (Visual Question Answering for Video Quality Assessment)ACM MM 2025 paper and code for VQA²-Assistant, a unified video/image quality scoring and interpretation model.
• CompressedVQA: Full/No-Reference VQA for Compressed UGC VideoICME 2021 grand challenge-winning full-reference and no-reference video quality models for compressed user-generated content.
• 2BiVQA: Double Bi-LSTM No-Reference Video Quality AssessmentNo-reference VQA model for UGC video using double Bi-LSTM, companion code to arXiv:2208.14774.
• VMAF-torchPyTorch reimplementation of VMAF metric, GPU/gradient-friendly for use in learned codec optimization loops.
• aws-batch-with-FFmpegReference architecture running FFmpeg containers (ARM64/x86-64/NVIDIA/Xilinx) on AWS Batch with Spot compute environments and SDK/REST job…
• Bitrate Ladder Construction via Transfer Learning and Spatio-Temporal FeaturesML-based bitrate ladder optimization achieving 94.1% complexity reduction at only 1.71% BD-rate cost using transfer learning.