MediaPerf: A Multimodal Video Understanding Benchmark Framework for the Media Industry

CoactiveAI's open-source MediaPerf framework is a multimodal video understanding benchmark tailored for the media industry. It aims to fill the gap where existing benchmarks are disconnected from real-world application scenarios, providing a production-grade solution for evaluating the video understanding capabilities of multimodal foundation models. The framework covers 16 mainstream models and 4 types of real business scenarios, and through its multidimensional evaluation system, it helps technical decision-makers comprehensively assess the feasibility of models in production environments.

With the rapid development of multimodal large models, video content understanding capability has become an important indicator of the practical value of AI systems. However, existing benchmarks are often disconnected from real application scenarios, making it difficult to truly reflect model performance in industrial environments. The MediaPerf project originated from observations of the actual workflow in the media industry. Addressing the limitation of traditional evaluations that only focus on single accuracy metrics, it builds a multidimensional evaluation system that considers industrial key factors such as latency, cost, and scalability.

MediaPerf defines four core task types: standard label tasks (topic recognition, sentiment analysis, etc.), label optimization workflow (simulating manual review iteration), video summarization tasks (generating editorial descriptions like storylines and intent communication), and summary quality assessment (using LLM to automatically evaluate summary quality). Performance metrics cover quality indicators such as accuracy and recall, as well as engineering indicators such as cost, latency, and throughput, helping to balance model quality and operational costs in decision-making.

MediaPerf supports 16 mainstream multimodal models, covering major cloud platforms such as AWS Bedrock, Google Vertex AI, OpenAI, Anthropic, and self-hosted Qwen models, including AWS Nova, NVIDIA Nemotron, Google Gemini, OpenAI GPT, Anthropic Claude, etc. The framework adopts a plug-in architecture, enabling zero-code extension through design patterns like Registry, Factory, and Builder. New models or tasks can be registered via configuration, reducing the threshold for maintenance and expansion.

The core dataset of MediaPerf is based on the 'Automatic Understanding of Image and Video Advertisements' project, containing 2003 advertising videos (with a total duration of over 29 hours, ranging from 30 seconds to 2 minutes and 30 seconds). Annotations are divided into basic (68 video-level tags, including topic/sentiment) and extended (long summaries covering storylines, creative intent, etc.) levels. Future plans include adding 100 new tag dimensions (type, format, etc.). The dataset eliminates tags with low coverage or low consistency to ensure evaluation credibility.

MediaPerf is developed with Python 3.12, using UV for dependency management. The core architecture includes a model layer (encapsulating API interfaces), a task layer (orchestrating evaluation processes), a metric layer (evaluation algorithms), and a storage layer (supporting S3/GCS/local storage). Configuration files are validated via Pydantic v2 to capture errors upfront and reduce resource waste; an intelligent caching mechanism supports frame-level reuse to avoid repeated decoding and transmission, with cache backends adapted to various deployment environments.

MediaPerf provides objective data basis for technical selection in the media industry, establishes unified evaluation standards, and supports performance comparison across different teams/projects. It extends evaluation dimensions to cost-benefit analysis, aligning with industrial decision scenarios. CoactiveAI plans to continuously expand the model range, add task types, and optimize evaluation metrics to keep pace with cutting-edge technologies.

The release of MediaPerf marks the transition of video understanding benchmarks from academic research to industrial application. Through design close to real business scenarios and extensive model support, it provides strong tool support for AI technology implementation in the media industry. It is an open-source project worth attention for technical teams that are evaluating or deploying multimodal video understanding systems.