Quantization Parameter Adjustment Using Variance and Encoding Cost
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Solution Overview
Problem
Block-based video encoding techniques face challenges in maintaining quality and bit rate control due to inherent lossiness and the need for quality compromises, particularly with the quantization parameter (QP), which affects spatial detail and distortion.
Innovation Solution
A rate control module dynamically adjusts quantization parameters based on sum of variances (SVAR) and estimated picture encoding cost (PCOST) metrics to optimize encoding for frame complexities, identifying scene features and adapting encoding modes for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization parameter (QP) is increased to reduce bit rate, then bit rate is lowered, but spatial detail is lost and distortion increases
Solution Approach 1:
The patent applies dynamics by making the quantization parameter adaptive rather than fixed. The QP is dynamically adjusted based on scene complexity metrics (SVAR and PCOST) calculated during encoding, allowing the system to optimize the balance between bit rate and spatial detail for each specific frame or sequence of frames.
Solution Approach 2:
The patent changes the parameter of quantization parameter (QP) based on calculated metrics. By computing SVAR (sum of variances) and PCOST (picture encoding cost) and using these to determine optimal QP values, the system adjusts the quantization parameter to achieve better compression efficiency while maintaining acceptable quality.
2Quantity of substance
If rate control is used to maintain target bit rate, then bit rate constraint is satisfied, but encoding complexity increases due to dynamic QP manipulation
Solution Approach 1:
The patent applies preliminary action by calculating SVAR and PCOST metrics during the encoding process to predict and determine optimal QP values in advance. This allows the rate control mechanism to prepare quantization parameters before encoding, reducing the complexity of real-time adjustments.
Solution Approach 2:
The patent uses feedback by calculating SVAR and PCOST metrics from the encoded data and using these metrics to adjust QP for subsequent frames. This closed-loop approach allows the system to learn from previous encoding results and optimize future encoding parameters, managing complexity through intelligent feedback-based adaptation.
3Manufacturing precision
If QP is adjusted to maintain quality, then spatial detail is preserved, but bit rate increases
Solution Approach 1:
The patent applies local quality by adjusting QP differently for different regions or frames based on their complexity. Scenes with high SVAR (high variance) and high PCOST are assigned different QP values compared to simple scenes, allowing optimal quality-bit rate tradeoff for each local region rather than using a uniform QP throughout.
Data Source
AI summary
A video processing device includes a rate control module to determine more accurate initial quantization parameters at each scene switching point and to adjust the QP parameters in response to scene changes using a sum of variances metric and an estimated picture encoding cost metric from a coding complex estimation block. To determine a first quantization parameter set, a sum of variances metric and an estimated picture encoding cost metric for an initial set pictures of a video stream are used. A bit allocation module is to set a target bit allocation for infra-encoded pictures as substantially proportional to the sum of variances metric and substantially inversely proportional to the estimated picture encoding cost metric, and set a target bit allocation for forward predictive and bi-predictive pictures as substantially proportional to the estimated picture encoding cost metric and substantially inversely proportional to the sum of variances metric.