Bitstream-Based Video Quality Prediction Without Reference Content
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Solution Overview
Problem
Existing video-quality prediction methods require reference video content or pixel-domain analysis, which is inefficient and resource-intensive, especially for remote monitoring and customer support optimization.
Innovation Solution
A neural network-based system that predicts video quality using high-level and block-level features extracted from the bitstream, without requiring the original video content, by employing a VVC decoder to parse syntax elements and extract features like transcode indicators, codec types, and block-level statistics, and converting RMSE values into quality metrics like PSNR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-domain analysis is performed on decoded video sequence, then video quality measurement accuracy is improved, but processing complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the necessary bitstream characteristics (quantization parameters, transform coefficients, motion vectors) from the encoded bitstream, avoiding the need to process the entire decoded video sequence in the pixel domain. This extraction approach maintains measurement accuracy while significantly reducing processing complexity.
Solution Approach 2:
The patent introduces bitstream characteristics as intermediary features that bridge the original video content and the quality prediction. Instead of directly analyzing pixel-domain data, the system uses these intermediate characteristics (QP, transform coefficients, motion vectors) as proxies that capture quality information without requiring full pixel-domain processing.
2Measurement precision
If reference video content is required for quality prediction, then prediction accuracy is improved, but system resource consumption and latency increase
Solution Approach 1:
The system performs self-service quality prediction by extracting all necessary information directly from the received bitstream and decoded blocks. The quality prediction is achieved using only the bitstream characteristics and decoded block data, without requiring external reference video content or additional resources.
Solution Approach 2:
Instead of using the actual reference video content, the patent creates a simplified representation (copy) of the quality information through bitstream characteristics. These characteristics (quantization parameters, transform coefficients, motion vectors) serve as compressed representations that capture quality essence without requiring the full reference video.
3Productivity
If block-level features are extracted from bitstream, then processing efficiency is improved, but feature extraction complexity increases
Solution Approach 1:
The patent segments the video processing into distinct blocks (coding blocks, slices, or tiles) and extracts features at this segmented level rather than processing the entire video stream uniformly. This segmentation enables parallel processing and reduces the complexity of feature extraction by handling smaller, manageable units.
Solution Approach 2:
The patent transforms the feature extraction process by changing from pixel-level parameters to block-level parameters (quantization parameters, transform coefficients, motion vectors). This parameter transformation simplifies the extraction process while maintaining the essential quality information, improving overall processing efficiency.
Data Source
AI summary
A system for non-reference video-quality prediction includes a video-processing block to receive an input bitstream and to generate a first vector, and a neural network to provide a predicted-quality vector after being trained using training data. The training data includes the first vector and a second vector, and elements of the first vector include high-level features extracted from a high-level syntax processing of the input bitstream.