Media Tools

About this collection

Media Tools brings together 704 curated resources from across the video technology landscape. Notable topics include AI & Machine Learning Tools, Ads & QoE, Audio & Subtitles, and Audio Analysis & Processing.

Resources (showing 121–144 of 704)

• madman-androidMadman (Media Ads Manager) is a high-performance alternative to Google's standard IMA Android SDK, allowing developers to render video ads…
• product_video_adsA solution developed by Google to build video ads at scale, enabling the creation of product video ads efficiently.
• pysrtpysrt is a Python library for parsing, modifying, and composing SubRip (SRT) files. It provides developers with tools to handle subtitle fi…
• pysubs2pysubs2 is a Python library for editing subtitle files, supporting various formats including SubRip (SRT), SubStation Alpha (SSA), and Adva…
• srtsrt is a simple library and set of tools for parsing, modifying, and composing SRT files. It offers a straightforward approach to handling…
• swiftsubtitlesA Swift package for reading and writing various subtitle formats, including SRT, SBV, SUB, VTT, CSV, LRC, and Podcast Index. It enables iOS…
• threefiveA Python library for parsing SCTE-35 in various formats, including MPEGTS, HLS, and DASH, facilitating ad insertion and management in video…
• truex-ad-renderer-web-integrationDocumentation and resources for integrating true[X]'s CTV web ad renderer, providing guidelines for implementing interactive ad experiences…
• v4l (Go Video4Linux)A Go library providing bindings to Video4Linux2 (V4L2) APIs, enabling video capture and camera control in Linux from Go applications.
• videojs-contrib-adsA Video.js plugin that provides a framework for creating ad integrations, offering a set of events and methods to manage ad playback and co…
• vigenairRecrafting Video Ads with Generative AI, this project leverages machine learning to enhance video advertisements, utilizing technologies li…
• web-monetisation-video-adsA utility that enables developers to monetize videos by utilizing web monetization when available and loading ads as a fallback through the…
• x9k3An HLS segmenter with SCTE-35 support, enabling live streaming from non-live sources and looping, useful for dynamic ad insertion scenarios.
• yt-dlpA command-line program to download videos from YouTube and many other video platforms. It's a fork of youtube-dl with additional features a…
• zveloDBzveloDB is a URL database that enhances brand safety in digital video advertising platforms. By integrating zveloDB, platforms can ensure t…
• AI-Youtube-Shorts-GeneratorLLM-driven highlight detection with Whisper transcription and automatic vertical cropping to turn long videos into shorts, MIT-licensed wit…
• Ad Insertion Sample (GitHub)The ad-insertion reference pipeline shows how to integrate various media building blocks, with analytics powered by the OpenVINO™ Toolkit,…
• An Overview of the JPEG AI Learning-Based Image Coding StandardIEEE overview paper describing JPEG AI's architecture and coding tools, the first standardized end-to-end learned codec.
• Audio TranscriberTranscodes audio & video files to text, supports MP3, M4A, WAV, MP4, MKV, AVI, MPG & MPEG. No Online API's. Python 3
• 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.
• CompressAI-Vision — Video Coding for Machines evaluation platformFramework to design and benchmark video-coding-for-machines pipelines, comparing learned codecs against VTM/HM/JM anchors with MPEG FCM sup…
• DQ-Ladder: Deep Q-Network Time/Quality-Aware Bitrate LadderDeep reinforcement learning (DQN) approach to bitrate ladder construction achieving 10.3%+ BD-rate gain and 22% decode-time reduction.
• Deep Reinforced Bitrate Ladders (DeepLadder)NOSSDAV 2021 paper on deep-reinforcement-learning-based bitrate ladder construction for adaptive streaming.
• Efficient Bitrate Ladder Construction for Content-Optimized Adaptive StreamingAcademic paper (Katsenou et al.) using ML to predict Pareto-optimal bitrate ladders from spatio-temporal content features.