RankDVQA — ranking-inspired deep video quality metric

Description

WACV 2024 project page for RankDVQA, a full- and no-reference deep VQA model trained with ranking-inspired hybrid training, with code and a distilled mini variant.

URL

• Visit resource

Related resources

• nr-vqa-consumervideoNo-reference video quality metric tuned for consumer video capture artifacts (sensor noise, motion blur, shake) rather than compression art…
• VQMTK: Video Quality Metrics ToolkitOpen-source cross-platform tool bundling 14 VQA metrics plus SI/TI spatial/temporal indicators, CLI or Jupyter web UI.
• ic-metricsOptimized C++ implementation of SSIMULACRA2/SSIM/MS-SSIM, roughly 25-30% faster via sub-score pruning.
• 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.
• 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.
• 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.
• 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.
• 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.