中文版本

Opportunity  

Content transcoding, the process of converting one encoded format to another, is widely used in digital media delivery, such as video streaming, video-on-demand, and live broadcasts. However, existing transcoding methods face significant challenges in maintaining consistent visual quality while adhering to real-time processing and buffer constraints. Traditional rate control algorithms for transcoding often rely on rate-distortion (R-D) models designed for raw content encoding, which do not account for the quantization effects of the initial encoding process. This leads to inaccuracies in bitrate control and distortion management, resulting in suboptimal visual quality, buffer violations, or excessive computational overhead. The need for efficient, real-time transcoding with minimal quality degradation motivates the development of this patent, which introduces a novel rate control scheme tailored for transcoding operations.

Technology  

The patent addresses the limitations of conventional transcoding methods by proposing a two-parameter rate-distortion (R-D) model that explicitly considers the quantization parameters (QPs) of both the input encoded content and the output transcoded content. This innovation ensures more accurate bitrate and distortion control during transcoding. The technology involves:  

1. Parsing Input Streams: The input content stream is parsed to extract QPs and frame bit usage information without full decoding, reducing computational overhead.  
2. Window-Level Rate Control (WRC): A two-pass rate control scheme is applied to a sliding window of frames (rather than the entire sequence), enabling real-time processing while maintaining smooth visual quality. The window size is dynamically adjusted based on buffer constraints.  
3. Sliding Window Buffer Check (SWBC): A buffer management mechanism ensures compliance with hypothetical reference decoder (HRD) constraints by iteratively adjusting QPs for each window of frames, preventing overflow or underflow.  
4. Dynamic R-D Model Updates: The R-D model parameters are updated iteratively during transcoding using actual bit usage statistics, improving accuracy over time.  

This approach combines the benefits of offline two-pass encoding (high quality) with the efficiency of real-time processing, making it suitable for applications like adaptive bitrate streaming.

Advantages  

• Improved Accuracy: The R-D model accounts for quantization effects in both initial encoding and transcoding, reducing bit control errors.  
• Real-Time Performance: Window-based processing enables efficient two-pass rate control without excessive latency.  
• Buffer Compliance: SWBC prevents buffer violations, ensuring stable streaming.  
• Consistent Visual Quality: Dynamic bit reallocation within windows minimizes quality fluctuations.  
• Computational Efficiency: Parsing input streams for QPs avoids full decoding during rate control.

Applications  

• Video Streaming Services: Adaptive bitrate transcoding for platforms like Netflix, YouTube, or live broadcasts.  
• Mobile Media Delivery: Optimizing video quality for devices with limited bandwidth or processing power.  
• Legacy Format Conversion: Transcoding between older (e.g., MPEG-2) and modern (e.g., H.265) codecs.  
• Cloud-Based Transcoding: Scalable processing for large-scale content delivery networks (CDNs).