imscED — IMSC subtitle/caption editor
Description
IMSC (TTML) subtitle/caption editor built on imscJS, with a Video Image Burner REST service for IMSC burn-in.
Resource details
- Category path
- Media Tools›Audio & Subtitles›Subtitles & Captions
- Provider
- Not yet classified
- Format
- Not yet classified
- Skill level
- Not yet classified
imscED — IMSC subtitle/caption editor is cataloged in Media Tools, Audio & Subtitles, and Subtitles & Captions. Provider: Not yet classified. Format: Not yet classified. Skill level: Not yet classified.
URL
Related resources
- SubalignerAutomatic subtitle synchronization tool using deep neural networks and forced alignment, with translation support.
- Normalize-AudioBatch EBU R128 loudness normalization tool for MKV files via ffmpeg, preserves video stream and caches loudness analysis.
- 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.
- MediaEditor Community (opencodewin)Node-based blueprint non-linear video editor with 45+ filters and 70+ transitions.
- A Gamut-Mapping Framework for Color-Accurate Reproduction of HDR ImagesAcademic paper (Sikudova et al.) proposing a gamut-mapping framework for color-accurate HDR image reproduction.
- aws-batch-with-FFmpegReference architecture running FFmpeg containers (ARM64/x86-64/NVIDIA/Xilinx) on AWS Batch with Spot compute environments and SDK/REST job…
- 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.
- 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.
- VMAF-torchPyTorch reimplementation of VMAF metric, GPU/gradient-friendly for use in learned codec optimization loops.
- 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.