By Megha Manohara, Anush Moorthy, Jan De Cock, Ioannis Katsavounidis and Anne Aaron
Overview
The article discusses Netflix's implementation of optimized shot-based encoding to enhance video quality while reducing bandwidth usage. It highlights the challenges faced during the integration of the Dynamic Optimizer framework into Netflix's production pipeline and presents the performance improvements achieved through this new encoding method.
What You'll Learn
1
How to implement shot-based encoding to improve video quality
2
Why using VMAF as an optimization objective enhances perceptual video quality
3
How to use collation and checkpoints to optimize encoding processes
Prerequisites & Requirements
- Understanding of video encoding and compression techniques
- Familiarity with encoding frameworks like Dynamic Optimizer(optional)
Key Questions Answered
What is the Dynamic Optimizer and how does it improve video encoding?
The Dynamic Optimizer is a framework that analyzes videos over multiple quality and resolution points to determine the best compression trajectory. It uses VMAF as the optimization objective, which helps generate video streams with enhanced perceptual quality while reducing bandwidth usage.
What challenges were faced when implementing shot-based encoding?
The main challenges included retrofitting the existing parallel encoding pipeline to handle significantly more encode units and managing the increased complexity of processing video on a per-shot basis, which resulted in a substantial increase in the number of chunks processed.
How does the new encoding method affect video quality at low bandwidth?
For members with low-bandwidth connections, the optimized encodes deliver higher quality video at the same or lower bitrate, resulting in fewer quality drops and rebuffers during playback. This enhances the overall viewing experience on constrained networks.
What are the benefits of using checkpoints in the encoding process?
Checkpoints allow for the immediate storage of each encoded shot and its metadata, ensuring that if a compute instance fails, only the remaining shots need to be re-encoded. This leads to significant computational savings and efficiency in the encoding pipeline.
Key Statistics & Figures
Increase in chunks processed per encode
More than two orders of magnitude
This increase was due to the transition from encoding video chunks of a few minutes to processing on a per-shot basis.
Bitrate savings with optimized encodes
Less than half the bits for AVCMain quality
VP9-Opt can stream the same quality at less than one third of the bits of AVCMain.
Quality improvement at 250 kbps
VMAF=79 for VP9-Opt
This demonstrates a significant enhancement in visual quality compared to AVCMain and AVCHi-Opt.
Technologies & Tools
Encoding Framework
Dynamic Optimizer
Used for optimizing video encoding processes.
Video Quality Metric
Vmaf
Serves as the optimization objective for encoding quality.
Key Actionable Insights
1Implement shot-based encoding to enhance video quality while reducing bandwidth usage.This approach allows for more granular optimizations, leading to better visual quality and efficiency, especially important for users on low-bandwidth connections.
2Utilize VMAF as an optimization objective for video encoding.By focusing on perceptual quality, you can ensure that the video streams delivered meet user expectations for visual fidelity, which is crucial for streaming services.
3Incorporate collation and checkpoints in your encoding workflows.These techniques help manage increased complexity and improve resilience in encoding processes, making them essential for high-quality video delivery.
Common Pitfalls
1
Failing to adapt existing encoding pipelines to handle increased complexity can lead to bottlenecks.
This happens when systems are not designed to process the significantly higher number of encode units introduced by shot-based encoding. Proper planning and implementation of new techniques like collation and checkpoints are essential to avoid these issues.
Related Concepts
Video Encoding Techniques
Compression Algorithms
Per-title Encoding
Dynamic Optimization In Streaming