Over the past decade, transcoding—converting media from one format to another—has been through a radical transformation. During my time at Rhozet (later acquired by Harmonic), when customers were migrating from tape-based workflows to file-based systems, the transition to newer codecs like H.264 was just beginning, and 2K (effectively Full HD) video was still a rarity. Back then, transcoding involved relatively basic tasks. Fast forward to today, and the process has evolved into a dynamic and sophisticated operation driven by technological advancements, higher-resolution content, and the rise of AI and cloud-based solutions. Here’s a breakdown of how transcoding practices have evolved over the past decade.
1. Quality vs Throughput: A Shift in Focus
Before
Encoding quality was the primary concern. Transcoding often led to visible loss of detail, such as pixelation and blurriness, particularly when converting between frame rates or resolutions. Speed was much slower than real-time, creating bottlenecks in workflows, and Full HD (1080p) was the highest resolution used.
Today
Advanced compression techniques have significantly improved transcoding effectiveness, and quality losses are much less of an issue. As a result, there’s been a dramatic increase in throughput, making transcoding more affordable and efficient. GPU compute, with its massively efficient parallel processing, has made faster than real-time transcoding the norm. Today’s transcoding systems can handle 4K and 8K resolutions, pushing the boundaries of media production.
2. Transcoding Features: Evolving Needs
Before
Transcoding workflows were simpler, focusing on basic tasks like frame rate or MXF conversion. Advanced video processing tasks, like watermarking, were often an afterthought. Each tool had a narrow, specific function, so multiple tools were needed to complete even a single task.
Today
Transcoders are now integrated into larger media workflows. They handle multiple tasks, including encryption, watermarking, and metadata generation. Today’s transcoders support a narrower range of formats but offer much deeper capabilities, including automatic ad insertion. Customers demand single, all-in-one solutions to handle these complex, multi-functional tasks.
3. Transcoding Workflow: From Isolated to Integrated Systems
Before
Transcoding was often done with on-premises Windows OS systems, with minimal integration with other media tools. Each step of the media process (editing, encoding, storage) was handled separately, resulting in inefficiencies and workflow delays.
Today
Transcoding workflows are now hybrid, combining on-premises and cloud-based solutions, providing greater flexibility and scalability. Transcoding tools support both Windows and Linux OS, allowing for a broader range of media environments. Today, transcoding sits at the core of a more integrated, end-to-end media workflow that spans everything from content ingest and editing to distribution and storage.
4. The Rise of Open-Source Codecs
Before
Ten years ago, media professionals were faced with a confusing array of codecs, some requiring paid licenses and some not (called open source). Although available for free, open source codecs were generally poor quality.
Today
Open-source tools have improved significantly, both in quality and reliability, to the point where they can be incorporated into enterprise-level media workflows. They still require transcoding management to do so, but they do offer the customer more choice.
5. Change in Transcoding Customers
Before
The primary customers for transcoding services were broadcasters and post-production houses, who required high-quality content for traditional media distribution.
Today
The customer base has expanded significantly. OTT platforms, IPTV providers, and streaming services now require transcoding to handle a wide variety of formats, resolutions, and the demands of both on-demand and live-streaming content.
6. Scalability and Dynamic Scaling: Adapting to Demand
Before
Transcoding workflows were static, designed to handle predictable, fixed volumes of content. Dynamic scaling wasn’t a major consideration, and systems struggled to adjust to fluctuating workloads.
Today
Modern transcoding systems are designed for dynamic scaling, able to adjust resources in real time based on customer demand. Cloud solutions have made it possible to scale up or down quickly, improving efficiency and cost-effectiveness. This scalability ensures that transcoding can meet varying demand without compromising performance or quality.
7. AI-Assisted Transcoding: Smarter, Faster, and More Efficient
Before
AI was not used in transcoding, which relied heavily on manual adjustments and basic encoding techniques.
Today
AI-powered transcoding tools are now standard in many workflows. These flexible, adaptable assistants help with metadata generation, automatic ad insertion, and even video quality enhancement by analyzing content and adjusting encoding settings. Machine learning detects and corrects visual anomalies, optimizing quality and reducing the need for manual intervention.
Conclusion
The transcoding landscape has evolved dramatically over the past decade. What was once a simple process of converting video formats is now a highly sophisticated, integrated function at the heart of modern media workflows. With improvements in encoding quality, speed, scalability, and the integration of AI, transcoding has become a key component of cloud-based media operations, helping to meet the growing demands of an increasingly diverse and global media market.
As the industry continues to push for higher resolutions, more formats, and smarter workflows, transcoding will evolve even further. With AI-driven workflows, dynamic cloud services, and the rise of open-source tools, transcoding is no longer a mere technical task. Instead, it’s a crucial part of delivering high-quality, scalable content across a wide range of devices and platforms. The next decade promises even more innovation, with transcoding poised to play a critical role in shaping the future of media.
