Video and Image Technologies in NetEase Cloud Music: Architecture, Algorithms, and Applications

Internet's rapid development has driven a sharp increase in demand and consumption of video and image content. Massive user bases and traffic have created diverse technical needs for video and image processing. This article explores the video and image technologies employed by NetEase Cloud Music, aiming to understand industry trends and how these technologies add value to business.

1. Background Introduction

With the fast growth of the Internet and widespread smart devices, video and image content consumption has exploded, especially on Cloud Music. The platform’s large user base generates varied requirements for video and image algorithms, which support creative content creation, social interaction, and efficient backend data processing. This article delves into the specific technologies used, their impact on user experience, music visualization, social features, and data handling.

2. Technical Architecture

The video‑image algorithms are tightly coupled with other system components to form a complete value chain. The architecture consists of four main modules: an algorithm training strategy platform, a foundational algorithm library, server‑side algorithm services, and client‑side algorithm engines. This design enables bidirectional communication between backend services and user clients, ensuring efficient integration throughout the business workflow.

3. Algorithm Directions

The foundational algorithm module can be categorized into four areas: content understanding, intelligent production, intelligent moderation, and video interaction.

3.1 Content Understanding

a. Video Classification – Uses cross‑modal methods that combine audio, visual, and textual information to classify long or short videos, addressing cases where visual cues alone are insufficient.

b. Sheet Music Recognition – An end‑to‑end image‑recognition pipeline that segments sheet music lines and applies transformer‑based models to extract high‑precision musical semantics, enabling conversion of paper scores to digital formats.

c. Playlist Recognition – Employs layout analysis, OCR, and NLP error correction to extract song information from playlist screenshots and automatically generate Cloud Music playlists.

3.2 Intelligent Production

a. Video Enhancement – Improves legacy or low‑quality videos by scene‑aware brightness and color adjustments, with special focus on facial regions for skin smoothing and detail preservation.

b. Smart Cover Selection – An AI‑driven pipeline that searches for optimal key frames, evaluates facial quality, and crops frames according to display requirements to produce the most appealing video cover.

c. Highlight Clip Extraction – Generates dynamic highlights by scoring video segments based on quality and business logic, selecting the most engaging clips for promotion or live‑stream recommendations.

3.3 Intelligent Moderation

Utilizes a database of 3,000+ celebrity faces and facial attribute analysis (detection, age, gender, attractiveness) to audit user avatars, reduce manual review costs, and combat fraudulent accounts through face clustering.

3.4 Video Interaction

a. Beauty & Makeup – Real‑time face detection, landmarking, and segmentation on mobile devices provide natural‑looking beauty effects and makeup filters, supporting low‑power, high‑stability operation across diverse hardware.

b. AI Effects – Real‑time AI‑driven visual effects enhance short‑video creation, offering creators a richer toolbox and increasing user engagement.

Additional video technologies exist but are omitted due to space and confidentiality considerations.

4. Future Outlook

The industry is entering a transformative era, especially with AIGC breakthroughs. Cloud Music will continue to explore multimodal audio‑video innovations, foster collaborations, and share resources to advance video and image technologies.

For more career opportunities, visit NetEase’s recruitment site: https://hr.163.com/