Adaptive Rate Model for Zero Block Detection in Video Encoding
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Solution Overview
Problem
Current video encoding techniques face inefficiencies in coding noisy video sequences, such as those from IP cameras, due to fixed rate estimation models that fail to accurately reflect the actual number of bits required, leading to higher bitrates and lower peak signal-to-noise ratios.
Innovation Solution
Adaptive rate modeling that updates parameters based on the actual number of bits used to encode non-zero coefficients and their positions, allowing for targeted zeroing out of blocks to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed rate estimation models are used, then encoding simplicity is maintained, but coding efficiency deteriorates leading to higher bitrates
Solution Approach 1:
The patent applies dynamics by transitioning from fixed rate estimation models to adaptive rate estimation models that dynamically adjust parameters based on actual encoding conditions. The model updates parameters a and b adaptively according to the number of non-zero coefficients and their positions, allowing the system to optimize coding efficiency while maintaining manageable complexity through automated adaptation.
Solution Approach 2:
The patent implements parameter changes by modifying the rate estimation model parameters (a and b) based on actual encoding statistics. The first parameter 'a' is updated based on the actual number of bits used to encode non-zero coefficients and their positions, while the second parameter 'b' remains fixed. This selective parameter adaptation improves coding efficiency without requiring complete model redesign.
2Device complexity
If fixed rate estimation models are used, then model simplicity is maintained, but bitrate increases
Solution Approach 1:
The patent applies dynamics by transitioning from fixed rate estimation models to adaptive rate estimation models that dynamically adjust parameters based on actual encoding conditions. The model updates parameters a and b adaptively according to the number of non-zero coefficients and their positions, allowing the system to optimize coding efficiency while maintaining manageable complexity through automated adaptation.
Solution Approach 2:
The patent implements feedback by using actual encoding results (number of bits used and positions of non-zero coefficients) to update the rate estimation model parameters. This feedback loop allows the model to learn from actual performance and adjust accordingly, reducing bitrate while maintaining model simplicity through incremental parameter updates rather than complete redesign.
3Productivity
If adaptive rate modeling with parameter updates is implemented, then coding efficiency improves, but model complexity increases
Solution Approach 1:
The patent implements parameter changes by modifying the rate estimation model parameters (a and b) based on actual encoding statistics. The first parameter 'a' is updated based on the actual number of bits used to encode non-zero coefficients and their positions, while the second parameter 'b' remains fixed. This selective parameter adaptation improves coding efficiency without requiring complete model redesign.
Solution Approach 2:
The patent applies segmentation by dividing the parameter update process into distinct components: the first parameter 'a' is updated adaptively based on encoding statistics, while the second parameter 'b' remains fixed. This segmentation allows the system to improve coding efficiency through selective adaptation without increasing overall model complexity, as only specific parameters require update mechanisms.
4Quantity of substance
If adaptive rate modeling is used, then bitrate is reduced, but computational overhead increases
Solution Approach 1:
The patent implements feedback by using actual encoding results (number of bits used and positions of non-zero coefficients) to update the rate estimation model parameters. This feedback loop allows the model to learn from actual performance and adjust accordingly, reducing bitrate while maintaining model simplicity through incremental parameter updates rather than complete redesign.
Solution Approach 2:
The patent applies segmentation by dividing the parameter update process into distinct components: the first parameter 'a' is updated adaptively based on encoding statistics, while the second parameter 'b' remains fixed. This segmentation allows the system to improve coding efficiency through selective adaptation without increasing overall model complexity, as only specific parameters require update mechanisms.
Data Source
AI summary
A video coding device may be configured to estimate, based on a combination of a first parameter and a number of non-zero coefficients in a frame, a number of bits for non-zero coefficients of the frame, to encode the frame based on the estimated number of bits for the non-zero coefficients, to collect an actual number of bits used to encode the non-zero coefficients of the frame and an actual number of the non-zero coefficients in the frame, to update, based on the actual number of bits used to encode the non-zero coefficients of the frame and the actual number of non-zero coefficients in the frame, only the first parameter to form an updated first parameter, to form a rate estimation model using the updated first parameter and a second parameter, and to select, based on the rate estimation model, a coding mode for each block in the frame.