Hardware Video Encoder Mode Decision Using Dual Quality Metrics
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Solution Overview
Problem
Conventional video encoding methods are inflexible in prioritizing quality metrics during mode decision, leading to suboptimal trade-offs between video quality and bit rate, and require complex processes for deriving suitable values for quality evaluation.
Innovation Solution
A hardware video encoding pipeline that includes a prediction module for generating primary and secondary encodes, a rate-distortion optimization module for determining costs, and a quality metric module for evaluating quality, allowing the decision module to select prediction modes based on both cost and quality metrics, thereby introducing flexibility in mode decision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video encoding methods use fixed quality metric thresholds for mode decision, then the encoding process is simple, but the video quality and bit rate trade-off is suboptimal
Solution Approach 1:
The patent implements dynamic adjustment of quality metric thresholds based on encoding conditions. The mode decision module compares multiple quality metrics (SSIM, PSNR, VIF) against adaptive thresholds that are adjusted according to the current encoding state, allowing the system to optimize video quality without requiring complex manual configuration.
Solution Approach 2:
The system changes multiple parameters simultaneously including quality metric thresholds, mode decision criteria, and encoding parameters. The mode decision module evaluates multiple prediction modes and selects the optimal mode by comparing quality metrics against dynamically adjusted thresholds, improving the quality-bitrate trade-off.
2Manufacturing precision
If the system evaluates multiple prediction modes with multiple quality metrics, then video quality improves, but processing time increases
Solution Approach 1:
The patent applies partial evaluation by comparing quality metrics against predetermined thresholds rather than exhaustively optimizing all parameters. The mode decision module performs partial mode evaluation by checking if quality metrics meet threshold criteria, avoiding full exhaustive search while still achieving improved video quality.
Solution Approach 2:
The system performs preliminary calculations of quality metrics (SSIM, PSNR, VIF) for each prediction mode before making the final mode decision. This preliminary evaluation allows the mode decision module to quickly identify promising modes and make informed decisions without requiring complete exhaustive analysis of all possible encoding parameters.
3Adaptability or versatility
If the system uses multiple quality metrics (SSIM, PSNR, VIF) for mode decision, then encoding flexibility improves, but computational complexity increases
Solution Approach 1:
The patent segments the quality evaluation into multiple independent metrics (SSIM for structural similarity, PSNR for signal-to-noise ratio, VIF for visual information fidelity). Each metric is calculated and compared against its own threshold independently, allowing the mode decision module to evaluate different aspects of video quality separately and make comprehensive decisions based on multiple criteria.
Solution Approach 2:
The mode decision module serves multiple functions by evaluating multiple prediction modes and comparing multiple quality metrics simultaneously. The system uses a unified threshold comparison mechanism that works across different quality metrics and prediction modes, providing a universal decision-making framework that handles diverse encoding scenarios.
Data Source
AI summary
An example disclosed system may include a hardware video encoding pipeline (HVEP), the HVEP that includes a prediction module that generates, using a primary prediction mode, a primary encode of a portion of a video stream, and using a secondary prediction mode, a secondary encode of the portion of the video stream. The HVEP may also include (1) a rate-distortion optimization (RDO) module that determines a primary cost associated with the primary encode and a secondary cost associated with the secondary encode, (2) a quality metric (QM) module that determines a primary QM associated with the primary encode and a secondary QM associated with the secondary encode, and (3) a decision module that selects, for an encoding of the video stream, at least one of the primary prediction mode and the secondary prediction mode based on the primary cost, the secondary cost, the primary QM, and the secondary QM.